FLEX Software User Guide v3 - Immucor Software/MIA... · 2017. 1. 30. · 10440-96, SR-800-10439-24...

Copyright © 2016, Sirona Genomics, Inc. All rights reserved.

FLEX Software User Guide v3.0

Advanced NGS HLA Genotyping Software

SR-790-00017

SR-850-00043

MIAFORA NGS FLEX Software Ver.3.0

Copyright © 2016, Sirona Genomics, Inc. All rights reserved. Page 2 of 103 SR-190-00523-EN Rev. A (12-2016)

Copyright Notice

This documentation and the MIA FORA NGS FLEX software are the confidential information of and are copyrighted, © 2015, by Sirona Genomics, Inc. All rights are reserved. No part of this documentation or the software may be reproduced, copied, displayed, transmitted, modified or used without the prior written permission of Sirona Genomics, Inc.

Disclaimer

MIA FORA NGS FLEX software has been CE marked in the European Union only for IVD use with MIA FORA NGS FLEX HLA Typing Kit Part Numbers SR-800-10433-24 (FLEX11), SR-800-10433-96 (FLEX11), SR-800-10441-24 (FLEX9), SR-800-10441-96 (FLEX9), SR-800-10440-24 (FLEX6), SR-800-10440-96, SR-800-10439-24 (FLEX5) and SR-800-10439-96 (FLEX5).

Trademarks

MIA FORA is a trademark owned by Sirona Genomics, an Immucor company.

All other trademarks and registered trademarks are the property of their respective owners.

All use of the MIA FORA FLEX software and this documentation are subject to the terms of the MIA FORA License Terms and Conditions available at www.immucor.com/miaforasoftwareterms. BY USING THE MIA FORA FLEX SOFTWARE AND/OR THIS DOCUMENTATION, YOU ACKNOWLEDGE THAT YOU HAVE READ, UNDERSTAND AND AGREE TO BE BOUND BY SUCH TERMS AND CONDITIONS AS THEY MAY BE UPDATED FROM TIME TO TIME.

http://www.immucor.com/miaforasoftwareterms



Table of Contents

1. Introduction to MIA FORA NGS FLEX Software ..................................................... 4 1.1 Genotyping Strategy .................................................................................................................... 4 1.2 Competitive Alignment to Reference Alleles ............................................................................. 5 1.3 Consensus Sequence Computation by Phasing ....................................................................... 6 1.4 Central Read Coverage ................................................................................................................ 7 1.5 Computed Genotypes .................................................................................................................. 8 1.6 Smart Flagging System ................................................................................................................ 8

2. Getting Started ........................................................................................................ 10 2.1 Logging In .................................................................................................................................... 10 2.2 Preference Panel ......................................................................................................................... 13 2.3 User Management ....................................................................................................................... 14 2.4 Disclaimer Information ............................................................................................................... 15 2.5 Laboratory Information .............................................................................................................. 15 2.6 Date and Time Format Information ........................................................................................... 15 2.7 Report Format ............................................................................................................................. 16 2.8 Review Options ........................................................................................................................... 17 2.9 Transferring Files between server and user’s machine using VNC Viewer ......................... 17

3. Projects Window ..................................................................................................... 19 3.1. Create a new project ................................................................................................................... 20 3.2. Launch Data Analysis ................................................................................................................ 25 3.3. Statistics Window ....................................................................................................................... 27

4. Review Window ....................................................................................................... 31

5. Detail Window .......................................................................................................... 32 5.1 Block A: Sample Information ..................................................................................................... 32 5.2 Block B. Genotype Table ........................................................................................................... 34 5.3 Block C. Variants, LD Suggestion, and Smart Guide .............................................................. 40 5.4 Block D. Allele Candidate Table ................................................................................................ 44 5.5 Block E1. Coverage Plots .......................................................................................................... 48 5.6 Block E2. Alignment Browsers .................................................................................................. 51 5.7 Block E3. Reference Alignment................................................................................................. 55 5.8 Regular Expression Filter .......................................................................................................... 62 5.9 Block F. Contig Alignment Browser ......................................................................................... 63

6. Summary Window ................................................................................................... 72

7. Auxiliary Tools......................................................................................................... 73

Appendix A: Software Color Codes Description ....................................................... 83

Appendix B: Glossary ................................................................................................. 88

Appendix C: Best Practices ........................................................................................ 90

Appendix D: Clickable Functions ........................................................................... 92

Appendix E: Additional Resources ............................................................................ 94

Appendix F: Third Party Libraries’ Licenses ............................................................ 95



1. Introduction to MIA FORA NGS FLEX Software MIA FORA NGS FLEX software delivers HLA typing information for the major Class I (HLA- A, B, and C) and Class II (DPA1, DPB1, DQA1, DQB1, DRB1 and DRB3/4/5) genes. Genotypes are computed from massive, paired-end sequencing reads derived from the Illumina Next Generation Sequencing (NGS) platform. The software provides accurate and phase-defined unambiguous HLA genotype information.

Figure 1-1: HLA Gene region showing relative locations of HLA Class I and Class II genes

HLA genes are one of the most complex regions of the human genome and the accurate and complete sequence of the HLA genes is an enormously complex endeavor. MIA FORA NGS FLEX software has been designed to take full advantage of the NGS technology and provide to users a simple-to-use tool to make the right decision for an unambiguous HLA typing call.

The software is designed to correctly identify HLA genotypes based on coding sequence. Special consideration is given to the most highly sequenced exon regions, i.e. exons 2-3 of Class I genes and exon 2 of Class II genes. At this time, less emphasis has been placed on identifying intron variants.

MIA FORA NGS FLEX software includes an intuitive graphical user interface (GUI) and complies with the requirements established by the HLA community. It provides to users: first, the accurate and unambiguous HLA genotypes based on the latest IMGT nomenclature and second, the complete phased sequence covered by the targeted primers used to interrogate the HLA genes.

1.1 Genotyping Strategy

MIA FORA NGS software combines two complementary informatics strategies to analyze each sample and then makes genotyping calls for target HLA genes using a computed confidence score. The first strategy ranks computed allele candidates based on mapping metrics. Coverage is calculated from competitive alignment of paired-end NGS sequence reads with all HLA reference sequences in the latest IMGT database

Class II Class III Class I

DP DQ DR B C A



and reference sequences produced by Sirona Genomics. The second strategy utilizes Phasing by Dynamic Program to assemble reads and construct phased assembled sequences. The approach is illustrated in Figure 1-2.

Figure 1-2: MIA FORA NGS genotyping strategy. Two complementary strategies are employed to compute the best fit to HLA reference alleles and resolve consensus sequences. The left side illustrates mapping. Paired end reads are mapped using a competitive alignment algorithm to rank candidate alleles. The right side illustrates assembly and phasing. Starting with paired end reads, a multi-step process includes mapping, local assembly, and phase resolution to construct phase resolved consensus sequences.

1.2 Competitive Alignment to Reference Alleles

Mapping is used to rank candidate alleles based on competitive alignment of paired end sequence reads with all HLA reference sequences to make sure that we capture all SNPs and structural variants. The reference database includes all HLA reference sequences in the latest version of IMGT HLA database and some generated through cloning and sequencing at Sirona Genomics. Two types of internally generated reference sequences are used in this tool: cloned and sequenced alleles and in silico sequences.



1) Cloned and sequenced alleles:

Cloned sequences are derived from individual long-range PCR products amplified from IHWG cell lines and samples. Thus, these sequences represent alleles that were completed by filling in missing intron and exon sequences. Cloned sequences in the database are indicated with one of three suffixes: e, v or x.

e: Sequences with the suffix e (e1, e2, etc.) are those with new intronic sequence not represented in the IMGT database. The vast majority of cloned sequences are of this type.

v: Sequences with the suffix v (v1, v2, etc.) are a small subset of cloned alleles that contain new intron variants relative to existing genomic sequences in the IMGT database.

x: Sequences with the suffix x (x1, x2, etc.) are a small subset of cloned alleles that contain new exon variants. The suffix is added to the closest known reference sequence but if confirmed by IMGT the allele name will change.

2) In silico sequences:

Many IMGT reference sequences contain partial exon sequences. To facilitate data analysis, the closest complete exon was copied to fill in the gaps in IMGT reference sequences with an incomplete exon. The suffix i (i1, i2, etc.) is used to identify those computationally filled sequences.

The extensions used in the naming of the reference sequences are to inform the user about the reference sequence that was used to make the allele assignment. The coverage statistics can be viewed for the IMGT and corresponding extended reference sequences. In all cases where the sequence has either been determined by actual sequencing or in silico extension of reference sequences, the HLA type can be reported in the accepted IMGT format without the extensions that are used for naming these extended sequences.

1.3 Consensus Sequence Computation by Phasing

A Dynamic Phasing algorithm generates one or two phased consensus sequences (contigs) by de novo assembly of mapped, paired-end sequences. In the same assembly process, polymorphic sites are identified where the minor allele frequency exceeds a threshold of 0.2. Once polymorphic sites are identified, Phased Resolved Consensus sequences (phased contigs) are built based on sequence assembly and polymorphic linkage.



Following Dynamic Phasing, the Phased Resolved Consensus sequences are aligned to the HLA allele database to determine the best fit. Consensus Alignment provides an independent check of the genotype call. Novel alleles are identified as discrepancies from the coding sequence of the reference alleles.

1.4 Central Read Coverage

Central read coverage was developed to ensure base calls can be made with a high degree of confidence. Central reads (Figure 1-3) are empirically defined as mapped reads for which the ratio between the length of the left arm and that of the right arm related to a particular point is between 0.5 and 2. When reads are mapped onto a correct reference sequence, they form a continuous tiling pattern over the entire sequenced region. However, when reads are mapped onto an incorrect reference sequence, they form a staggered tiling pattern at some positions of the sequenced region. To quantify this difference between the two alignment patterns, the numbers of “central reads” are counted for any given point. Central Read Coverage ensures that potentially mismatched reads are excluded.

Figure 1-3: Central reads of an anchor point are defined as mapped reads, where the ratio between the length of the left arm and that of the right arm related to a particular point is between 0.5 and 2. The left side of plot shows the mapping pattern of reads onto two correct references and one



incorrect reference. The incorrect reference has a mosaic pattern at the two different positions between two correct reference sequences. From the plot, it can be seen that reads with an even tiling pattern are mapped to the correct reference sequence while reads mapped to the incorrect reference sequence have an uneven, staggered pattern. The coverage plot graphs on the right side illustrate an example where central read coverage can distinguish a correct reference from an incorrect reference, while regular coverage cannot.

1.5 Computed Genotypes

For each gene, confidence scores are used to identify the best matching alleles in the reference database. Key coverage statistics are combined in a proprietary algorithm to calculate confidence scores and select the top computed alleles for each gene. The best allele candidates are then selected as the computed genotype call. Key components of the confidence score are illustrated in Figure 1-4. Coverage statistics such as number of mapped reads and minimum coverage are calculated to identify the best match to the HLA reference allele database. Phase-resolved consensus is determined through sequence assembly and polymorphic linkage. Candidate pairs are also evaluated and included in the calculation.

Figure 1-4: Key components of confidence scores used to rank allele candidates. Confidence scores are calculated using a combination of mapping, phase-resolved consensus, central read coverage, and candidate pair metrics.

1.6 Smart Flagging System

A Smart Flagging System was developed to display different information about the genotypes for all the genes in the selected sample. Above each allele are four to five shape indicators. Symbols for each of the indicators are:

• Triangle - confidence score

Confidence Score

Mapping

Central Coverage

Phase Resolved Consensus

Candidate Pair



• Diamond - common or well-documented (CWD) allele • Pentagon – overwritten automatic call • Hexagon - consistency with linkage disequilibrium data • Circle with a question mark or exclamation mark – Question mark indicates a

mismatch in the coding sequence (disconcordant) between the selected allele and the closest phased resolved consensus. An exclamation mark indicates that the allele is in the double-check list which requires careful manual review. The double-check list includes alleles that can have issues in sample/library prep, sequencing or data analysis. The circle will turn green after reviewed and confirmed by the user.

• PC - the number of phased resolved de novo contigs.

See the legend in Figure 1-5 for further explanation of each shape.

Figure 1-5: Flags (colored shapes) are used to depict each predicted genotype status. Indicators for confidence score (green triangle > blue > red > gray) where green is the highest confidence score and grey is the lowest confidence score, common or well-documented allele (green diamond) or not (amber diamond), whether a call has been edited (amber pentagon) or not (green pentagon), whether a call is consistent with linkage disequilibrium data (green hexagon) or not (amber hexagon), and whether special review is required (amber circle with question mark or exclamation mark), indicating a potential novel allele in the coding sequence. PC: the number of phased contigs. If the count of contigs is different from the number of alleles of the corresponding locus, it will show amber color in the circle. Otherwise, it is green color.



2. Getting Started The MIA FORA NGS software is an accessory for use with Illumina next generation sequencing (NGS) data obtained by sequencing libraries prepared by targeted amplification of the intended genes using the MIA FORA NGS HLA typing kit. Due to the complex nature of HLA testing, qualified laboratory personnel must review any results to assure accuracy.

The software can be accessed through either a VNC Viewer connection or direct access through monitor and keyboard attached to the server. To access the server directly, log in using KDE plasma workspace mode instead of GNOME classic mode:

1. Turn ON the server and wait till logon screen appears. Once logon screen appears, click on User Account to log in.

2. Click on Username and enter the Password. Before clicking on the Sign-in button, click on the Gear icon next to the Sign-In button and select “KDE plasma workspace” from the list. Then click on the Sign In button to login to Red Hat Linux 7.

3. Follow the instructions to log in and access MIA FORA NGS software as described below.

2.1 Logging In

VNC Viewer can be downloaded from https://www.realvnc.com/download/viewer/. Follow the instructions on the website to install VNC Viewer on your computer.

Open the VNC Viewer application by clicking on the VNC icon. Choose the appropriate VNC Server name or IP address and click on Connect as shown in Figure 2-1. Please note: due to software updates, VNC Viewer may look different. At the next prompt, enter your unique login name and password.

Figure 2-1: VNC login screen. Choose the appropriate VNC Server name or IP address and click on Connect button. At the next prompt enter login credentials. Each user needs appropriate access privileges and a unique username. After three failures, the login will close, and the user will have to re-open the window and begin the login process again.

https://www.realvnc.com/download/viewer/



After logging into VNC Viewer the remote desktop will be displayed. The VNC Viewer may be configured for many functions, such as exporting and printing data. The latest version of the VNC User Guide can be found at http://www.realvnc.com/products/vnc/documentation/5.2/guides/user/VNC_User_Guide.pdf.

To access MIA FORA NGS software, follow the steps illustrated in Figure 2-2.

Step 1: Hover over the tab at the top of the window to reveal the pull-down menu and select Full Screen Mode.

Step 2: Click on the MIA FORA icon found in the Quick Start menu.

Step 3: Enter login credentials. The password must be between 4-8 characters and contain at least one lowercase letter, one uppercase letter and one number.

When logging into MIA FORA software for the first time, labdirector is the default user to this software. Select labdirector as the username, and type default password provided by Immucor. After that, an End User License Agreement window will show up, which the user needs to read before clicking the Agree button to continue. The preference panel will show up to allow labdirector to add new user, provide lab information such as lab contacts, and disclaims. The preference panel continues to display until all information has been entered.

Figure 2-2: Steps for logging into MIA FORA NGS software from VNC Viewer. First, maximize the window to fit the computer display. Second, click on the MIA FORA icon either from the Quick Start menu or from the icon on the desktop. Third, enter MIA FORA NGS software login credentials.



Figure 2-3: End user license agreement dialog shown up when a new user log in first time.

The main window of MIA FORA NGS software, as illustrated in Figure 2-4, will be displayed after logging in. Return to this page at any time by selecting the Projects icon in the upper left-hand corner.



Figure 2-4: Main MIA FORA NGS FLEX window. Projects that have been created are listed in this main page. Return to this view at any time by selecting the Projects icon. Software commands can be accessed through the side panel and dropdown menus: three drop-down menus at the top of the home screen with eight shortcuts under Tools; an icon list along the top left side to open data views; eight icons along the bottom left side for additional project commands; two buttons on the bottom right to create or delete projects. User name, current project, current sample, current gene, storage capacity in percentage, and last action taken will be displayed in the status bar at the bottom of the window once project is created.

2.2 Preference Panel

A Preference window with user management and account tools will be displayed the first time a new lab director user logs in. Bypass the Preference window by clicking on “X” in the upper right corner to close the window. For users accessing software directly from server, press button on keyboard to close the window. The preferences menu can also be accessed from the top menu under File>Preferences, shown in Figure 2-5.

Figure 2-5: Top menu toolbars. There are three drop-down menus for accessing File, Tools and Help functions



In addition, if the laboratory information and disclaimer information are not set and the logged in user has the director role, the preference panel will show up as in Figure 2-6. Until all the information in the preference panel window is filled in, it will pop up every time a user with director access opens the software.

2.3 User Management

User management tools can be accessed through the User panel in the Preference window, shown in Figure 2-6. When creating new accounts for users, it is important to remember the privileges and abilities of each role. A user with the lab director role can perform all actions with full access to the software. A lab technologist can perform most of the tasks except for confirming a sample and deleting a project. A guest has read only access; therefore, they cannot make any edits to allele calls when reviewing a project. A guest user also cannot create, delete, export, or import projects. Detailed permission for the three user types are shown in Table 1.

Check Results

Make Comments

Change Call Approve Confirm

Delete Project

Set Reference

Lab Director ✔ ✔ ✔ ✔ ✔ ✔ ✔

Technologist ✔ ✔ ✔ ✔

Guest ✔ ✔

Table 1: Permission for user roles, Director, Lab Technologist and Guest

To add a user, select the role, enter a login name and email address and create a password. The password must contain at least 8 characters including at least one upper and one lowercase letter and one numeral. Only a lab director may delete a user.

Figure 2-6: Preference window of setting up new accounts, delete user or change password of current user. Note that only user with director role has the privilege to add or delete user.



2.4 Disclaimer Information From the preference window, the user can set up new accounts and default parameters, including laboratory information and disclaimer language to be used in the HLA typing report, shown in Figure 2-7.

Figure 2-7: Disclaimer information panel displays the disclaimer information for the final report. A user with lab director privileges can edit this information.

2.5 Laboratory Information

Figure 2-8: Laboratory information panel displays laboratory contact information to be shown in final report.

2.6 Date and Time Format Information

Figure 2-9: Date and Time format panel for setting date and time formats.



2.7 Report Format

Figure 2-10: Report format options.

For Lab directors, there are six options in the Report Format window: “Number of Field”, “Allele extension”,” Show comment”, “Show QC metrics”, “A4 Report Page Size” and “Sort Ambiguous Allele Numerically”. Number of Field allows the Lab director to set the number of fields that will be reported. Allele extensions are those suffixes (e, i, v and x) added to the names of reference sequences generated internally either through cloning-and-sequence or computationally. “Allele Extension” option will allow users to add allele extension suffixes to the allele name in the final report. For example, when “Allele Extension “box is unchecked, A*01:01:01:01v1 will be displayed as A*01:01:01:01 in final report. “Show comment” option allows users to display comments created during a review process in the final report. “Show QC metrics” allows the lab director to show or hide the sample QC metrics in the report. “A4 Report Page Size” option allows users to change the page size to A4 format (European format). “Sort Ambiguous Allele Numerically” allows the lab director to enable or disable the sorting of ambiguous alleles numerically.



2.8 Review Options

Figure 2-11: Review Options panel

Review option tab in the preference window allows a user to set configurations in the detail and review window. The default order of candidate alleles can be set to either order by Call column or by cReads column in the genotype table. To order the candidate alleles by cReads, tick the preference and select Apply. The default order of loci can be set to Alphabetical order or conventional order. The conventional order is A, B, C, DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1. To order the loci alphabetically, tick the preference and select Apply.

2.9 Transferring Files between server and user’s machine using VNC Viewer

VNC Viewer has a built-in File Transfer function. The File Transfer feature is useful for uploading sample sheets and downloading reports. Caution is advised because this function has the ability to transfer individual files or entire folders. Select only the desired file to avoid transferring an entire folder instead of a single file.



Figure 2-12: VNC Viewer File Transfer window.

To fetch files from the VNC to the user’s computer:

1. Locate the VNC icon in the lower right corner of the VNC viewer window. 2. Right click on the VNC icon to open a menu of options. 3. Select File Transfer from the list of options. 4. The file transfer dialog box will appear as shown. 5. Choose the option Fetch files to: “Ask every time.” 6. Click on the button labeled Send files to open a new window and view your files. 7. Select the file to transfer. 8. Click on the OK button to transfer the selected file to the download location

specified above. 9. A message will appear with a list of files transferred and their Download status.

Files can also be loaded directly to the server using a USB Flash Drive.

To transfer files to the VNC Server from the user’s computer:

1. Hover over the top center of the VNC viewer window, and a menu will drop down.

2. Locate and press the File transfer button and the file transfer window will be displayed.

3. From the pull-down menu in the lower right corner of the window, “Fetch Files To”, select the location of the files to be transferred.

4. Choose the files to be transferred and press the Send files button in the lower left corner of the window.

A message will appear when the file transfer was completed successfully.

Files can also be loaded directly to the server using a USB Flash Drive.



3. Projects Window

Click on the project button to show a table of all projects created by users. Each row displays one project with 15 columns: Project Name, Created on, Analyzed on, Lot, Received on, Operator, Project Status (Sample, Analysis, Review, Approval, Report), Next Step, Software Version, IMGT version and Run Mode. To refresh the page, click on the Projects icon.

Figure 3-1: Projects window. Displayed after clicking on the Projects button on the left panel or after successful login.

The complete project name will be generated once the project is created, which consists of three parts: the project name provided by user, the date the project is created and a randomly chosen English bird name. For instance, test_10Dec15_MURRE, where test is provided by user through new project wizard, the second and third parts are added by the software. Use the complete project name generated by MIA FORA software when setting up an Illumina run and make sure the Experiment name in the sample sheet matches the Project name generated by the software.

The status column is shown as Figure 3-2. There are five steps to complete each project: Sample, Analysis, Review, Approval and Report. Each one is represented by one “moving next” shape. Each status has four states which are color-coded: amber (ready



for next step), red (failure), blue (in progress) and green (completed). When the fastq files are being generated the first arrow becomes blue and when the fastq files are completed, the arrow turns green. When the analysis is progressing the second arrow turns blue. When the analysis is complete for all samples the second arrow turns green and the third arrow turns orange. The third arrow is an indicator for review.

Note: In the FLEX software the data can be reviewed as they become available for review. This is indicated when the third arrow is dark gray, and when all the samples are ready it becomes orange. Once the review has started, the arrow stays blue until all samples have been reviewed. The fourth arrow becomes green when all the samples have been approved and confirmed. The fifth arrow will turn orange once all the samples have been approved and confirmed.

Figure 3-2: Project status indicators. Amber indicates ready for next step; red indicates an error; blue indicates step in progress; green indicates step is finished.

The next step button (green arrow) serves two purposes. Once a new project is created, clicking on the next step button will launch the fastq files upload wizard if the automatic import was not set up during the run by matching the project name and the experiment name. Second, if a project analysis failed, clicking on the next step button will change the status back to ready.

3.1. Create a new project

To create a new project and submit the sequencing results for HLA typing, click on the New Project button in the lower right-hand corner of the main Projects page. See Figure 3-3.



Figure 3-3: New Project initiation. Select button in lower right corner of the main application page.

After clicking on the New Project button, a dialog box will appear as shown in Figure 3-4. Click Next to display the dialog box shown in Figure 3-5. Enter project name, which is created by the user, MIA FORA kit lot number and date that kit is received. An example is shown in Figure 3-6. Choose the run configuration and click Next. The software will add both the date when the project is created followed by one of the random bird names to create a unique, complete project name. The Sample Barcodes will be displayed as shown in Figure 3-7. Click Yes to confirm the sample names. The project conclusion page will be displayed as shown in Figure 3-9.

FLEX kit configuration contains 5, 6, 9 and 11 loci modes. In addition to the regular Clinical configuration, there are FLEX5, FLEX6, FLEX 9 and FLEX11 configurations and a Custom option for the user to manually specify a subset of loci to be analyzed.

The sample sheet must be a windows formatted CSV file that has the sample name in the first field and the corresponding barcode ID in the second for the test kit 24. The sample sheet must be a windows formatted CSV file has the sample name in the first field and the plate number and well position in the second field for the test kit 96. The barcode sheet templates are displayed in Figures 3-7 and 3-8.

Note: Plate03 should be the default as the plate number.

See the MIA FORA NGS HLA Typing Kit FLEX User Guide for instructions on how to create the Sample Barcode sheet.



Figure 3-4: Welcome page of new project wizard

Figure 3-5: Project information dialog box of new project wizard

Insert figure showing the options listed in the drop-down menu for Run Configuration.



Figure 3-6: Filled dialog boxes on project information page of new project wizard.

Figure 3-7: Sample barcode confirmation page of new project wizard for test kit 24.



Figure 3-8: Sample barcode configuration page of new project wizard.

Figure 3-9: Summary page of new project wizard. Note the project name is computationally generated with three parts: the part provided by user, current date and a random bird name.



3.2. Launch Data Analysis

Once configured correctly with the project name matching the Experiment name in the MiSeq or MiniSeq run, the FLEX software is designed to start data analysis automatically once the sequencing run is done. The software will generate the fastq files from the sequencing raw data and launch the analysis pipeline after the generation of the fastq files. However, if a user wants to analyze fastq data from previous experiments, make a new project starting with the letter x. This will make sure that the fastq files can be loaded without the need for matching the project name with the fastq files. Load the fastq data files into the project by clicking on the Next Step button. The fastq files wizard will open and navigate to load the fastq files (Figure 3-10, Figure 3-11 and Figure 3-12). Hold down key to select both fastq files together.

Figure 3-10: Welcome page of fastq files upload wizard.

Figure 3-11: Filled input box of fastq files wizard showing files selected.



Figure 3-12: Confirmation page of fastq files wizard.



3.3. Statistics Window

The statistics are available as soon as the demultiplexing is done, even if the samples are not yet ready for review. The Statistics icon will be highlighted when ready for review. Statistics window displays sequence quality of the selected project. Sequencing quality metrics from the selected project are shown as well as read distribution of barcoded samples, insert distribution, and pie chart of valid reads.

Click on the Statistics button on the top left sidebar will bring up the statistics window for the selected project (Figure 3-13).

Figure 3-13: Statistics window displaying sequence quality of the selected project. Sequencing quality metrics from the selected project are shown as well as read distribution of barcoded samples, insert distribution, and pie chart of valid reads.

Figure 3-14 shows the overall read distribution for three categories: invalid barcodes, unused barcodes and valid barcodes. Reads with sequencing errors in the barcode or contaminated barcode will be classified under Invalid Barcode. When a run consists of 24 samples but only a subset of samples is analyzed, any reads that are not analyzed will be classified under Unused Barcode.



Figure 3-14: Pie chart of sequence reads distribution among invalid barcode, unused barcode and

valid barcode reads categories.

Figure 3-15 insert distribution shows the distribution of end-to-end distance between paired-end reads after mapping onto reference sequences.

Figure 3-15: Insertion size of paired end reads distribution.

Figure 3-16 and Figure 3-17 show the read count for each barcoded sample in either histogram or plate layout. The plate layout allows the user to diagnose a failed run. By clicking Export or Export All, the barcode distribution table can be exported and saved. All other graphs on the statistics page can be saved in the same way.



Figure 3-16: Bar graph of read count for each barcoded sample.

Figure 3-17: Plate view for sample quality.



Figure 3-18 shows the distribution of nucleotides along each position of the sequencing read. The first nucleotides of the graph represent the barcode region where the distribution is distorted.

Figure 3-18: Distribution of nucleotides along each position of read.

Figure 3-19 shows the distribution of quality scores at each position along the read.

Figure 3-19: Distribution of sequencing quality scores along each position of read should above

Q30.



4. Review Window Review Window provides a simplified view of the genotyping results and quality metrics, all of which are explained in Chapter 5: Detail Window. Some of the features can only be accessed in the Detail view.

Figure 4-1: Annotated review window. Block A displays sample information; Block B displays the selected genotypes for the sample. Block C displays the Smart Guide and LD Suggestion tables; Block D displays the table of computed allele candidates; Block E displays the table of computed allele candidate pairs; Block F displays coverage plots and alignment browsers across the coding and non-coding sequencing regions.



5. Detail Window

The samples are available for review as they become available for review. Click on the Detail button on the left side bar brings up the Detail Window as shown in Figure 5-1. It is important for reviewers to verify automatic calls for flagged samples and make edits where necessary. Edits are necessary when independent software algorithms are inconsistent in calling novel alleles, in detecting polymorphisms, or in phasing.

Figure 5-1: Annotated detail window. Block A displays sample information; Block B displays the selected genotypes for the sample. Block C displays the Variants, Smart Guide, and LD Suggestion tables; Block D displays the table of computed allele candidates; Block E displays coverage plots and alignment browsers for mapped sequence of sample; Block F displays the alignment browser for phase resolved de novo contigs.

5.1 Block A: Sample Information

The sample information block contains Sample, Locus, and last visited date (Figure 5-2). The Sample dropdown lists barcode ID and sample name. The Locus dropdown lists HLA loci. Last visited field displays the date and time of the last visit. The Sample QC button will show the sample level QC metrics, which can be exported in PDF format. A user can view different samples or genes through the dropdown lists, as shown in Figure 5-3 and Figure 5-4.



Figure 5-2: Sample information panel in detail window.

Figure 5-3: Sample information dropdown list. The background color indicates the review status of each sample: gray indicates the sample has not been reviewed yet (visit time = 0); yellow indicates 1 to 10 visits; blue indicates more than 10 visits; green indicates the sample was approved. The last visited sample is indicated with red text.

Figure 5-4: Locus dropdown list sorted alphabetically.



5.2 Block B. Genotype Table

The sample genotype table (Figure 5-5) displays the selected genotype for the sample. The phasing consensus (PC) column lists the number of contigs built by the de-novo assembly algorithm. Each Allele column lists alleles predicted to be in the same haplotype. There are two buttons above the genotype table: Approve and Confirm, which provides two level review option.

Figure 5-5: Genotype table for a single sample before approval. The Smart Flag is displayed above each allele. Alleles are organized by predicted haplotype; each column represents one predicted haplotype. Each cell of the table is clickable to switch to the review panel for selected gene. The highlighted alleles show automatic calls that should be manually reviewed. The legend for Smart Flagging System can be shown by hovering cursor over the headers for allele columns.

The Approve button allows the Director or Lab Technologist to approve the results. Once the Approve button is clicked, a comment dialog will be shown as in Figure 5-6. The user can cancel the action by clicking on the Cancel button. The user can add a comment in the New Comment window. Click on the OK button to load the comment into the project log. After the sample is approved, the Approve button will change to UnApprove.



Figure 5-6: Comment dialog box for sample approval

The Confirm button allows only the lab director to confirm the results. Once the Confirm button is clicked, a comment dialog loaded with previous comments will be displayed. The user can cancel the action by clicking on the Cancel button. The user can add a comment in the New Comment window. Click on the OK button to load the comment into the project log. After the sample is confirmed, the Confirm button will change to UnConfirm and the Report button will appear next to the UnConfirm button (Figure 5-7).

Figure 5-7: Genotype table of alleles for a sample after approval. Three buttons are displayed

(UnApprove, UnConfirm, and Report.)

Click on the Report button to display a report dialog box (Figure 5-8). If there is ambiguity in the two alleles from DPB1, the equivalent pairs of alleles will be displayed in the last column “Notes”. Two options (PDF and XML) allow the user to choose a different output



format. Enter output file name in a file dialog and save the file. If the Report Format preference has been selected to show comments the PDF output file will display all comments related to the sample at the bottom. Similarly, if the Report Format preference has been selected to show allele extensions, they will be displayed in the report. Examples for each of the files formats are shown in Figure 5-9, Figure 5-10 and Figure 5-11.

Figure 5-8: An example of a report dialog box. One (legacy XML) button at the bottom left corner and two (PDF and XML) buttons at the bottom right corner allow the user to choose the format of reporting results. User can choose in the preference panel whether or not to have comment and allele name extension (e, i, v, x) displayed, or A4 page format.



Figure 5-9: Example of a report in PDF format with both comments and allele name extensions (Preferences are set to ‘Show Comments’ and show ‘Allele Extensions’).



Figure 5-10: Example of a report in PDF format without comments and allele name extensions.



Figure 5-11: Example of a report in legacy XML and XML format.

5.3 Block C. Variants, LD Suggestion, and Smart Guide

There are three tabs in this block: Variants, Linkage Disequilibrium suggestion (LD info), and Smart Guide. In the Variants tab (Figure 5-12), the first column (Pos) lists polymorphic sites of de-novo assembled contigs. The second column (Depth) provides the coverage depth of each polymorphic site. The number outside the bracket is the total number of reads covering that site; the number within the bracket is the number of nucleotides not listed in either contig. The third column (Contig1) lists the nucleotide and its frequency in contig 1 and the fourth column (Contig2) lists the nucleotide and its frequency in contig 2. The fifth column (Block) indicates phasing blocks; each block is a continuous region which can be phased with support from paired-end reads; each phased block begins and ends with the ‘--’ symbol.



Figure 5-12: Variants table. The first column (Pos) lists the position of each polymorphic site in the de-novo assembled contigs. The second column (Depth) lists the coverage of each polymorphic position. The number outside the bracket is the total number of reads covering that site; the number within the bracket is the number of nucleotides not listed in either contig. The third column (Contig1) lists the nucleotide and its frequency in contig 1 and the fourth column (Contig2) lists the nucleotide and its frequency in contig 2. The fifth column (Block) indicates phasing blocks; each block is a continuous region which can be phased with support from paired-end reads; each phased block begins and ends with the ‘--’ symbol highlighted with amber.

The block highlighted with amber, as shown in Figure 5-13. Amber highlighted entries in Contig1 and Contig2 indicate positions where the ratio of the nucleotide frequencies deviates from the average ratio across the sample. These polymorphic positions are unreliable.



Figure 5-13: Variants table. Amber highlighted entries in Contig1 and Contig2 indicate positions where the ratio of the nucleotide frequencies deviates from the average ratio across the sample.

The LD info tab (Figure 5-14) displays LD Suggestions based on aggregated information from the publicly available database and from analysis of internally typed samples. The public haplotype data can be found at: https://bioinformatics.bethematchclinical.org/HLA-Resources/Haplotype-Frequencies/

Each row represents alleles that have been observed to be associated with one another. Alleles present in the computed calls are highlighted in yellow. The LD suggestion panel is for information only; LD is not used to make automatic allele calls.

https://bioinformatics.bethematchclinical.org/HLA-Resources/Haplotype-Frequencies/



Figure 5-14: LD Info tab displays LD Suggestion.

The Smart Guide (Figure 5-15) provides guidelines for reviewing the HLA typing results. Possible reasons may be provided to explain why a user should review the specified computed genotype highlighted in pink. The recommended actions are described to provide instructions for review. The smart guide will only contain information when an automatic allele call is highlighted in pink in the genotype table on the review page.

Figure 5-15: Smart Guide lists Reasons and Actions for reviewing allele calls.



5.4 Block D. Allele Candidate Table The allele candidate table (Figure 5-16) lists the automatic allele calls and parameters calculated from sequence reads mapped to HLA reference sequences. Within this table, a user is allowed to comment on a selected allele, overwrite an automatic allele call and make a manual allele call. The Call Column (Call) contains a check symbol to indicate the automatic or manually selected alleles. The green check symbol indicates that there is no warning associated with the selected allele. The amber check symbol indicates a warning associated with the selected allele. The blue symbol in the second column indicates there is a comment associated with the corresponding allele.

After comparing the best-matched contig with the reference allele, the software displays the number of mismatched nucleotides between the contig and the reference allele in the exon (MME) or intron (MMI). The alleles highlighted in gray indicate a candidate best matched with contig 1. The alleles highlighted in amber indicate a candidate best matched with contig 2.

There are four different sets of reference sequences: cRead (cDNA), eRead (partial cDNA, exons 2 and 3 for class I and exon 2 for class II loci), gRead (genomic DNA), and xRead (gRead minus the eRead). For each set of reference region, three quality metrics are calculated: total number of mapped reads, minimum overall coverage (Cov) and minimum central coverage (Cen). All 12 quality metrics are displayed for each of the candidate alleles in the table. The tooltip for metric definition can be displayed by hovering cursor over the header for each column. Any null alleles will appear in the candidate table alongside their corresponding alleles.

Figure 5-16: Example of candidate table. This table of 17 columns lists likely allele candidates for the corresponding locus of the sample and their mapping parameters. The first column (Allele) is the allele name of candidates. The second column (Call) with check symbols indicates the automatic or manually selected alleles. The third column (Cmt) shows whether there is comment associated with the particular allele. The fourth and fifth columns (MME, MMI) list the number of mismatched nucleotides in the exon or intron after comparing the best-matched contig with reference allele; the alleles highlighted in gray indicate a candidate best matched with contig 1. The alleles highlighted in amber indicate a candidate best matched with contig 2. Columns 6-8 (cRead, Cov, Cen) list the mapping parameters against cDNA reference sequences. Columns 9-11 (eRead, Cov, Cen) list the mapping parameters against partial cDNA reference sequences,



specifically Exons 2 and 3 for Class I loci and Exon 2 for Class II loci. Columns 12-14 (gRead, Cov, Cen) list the mapping parameters against genomic reference sequences. Columns 15-17 (xRead, Cov, Cen) list the mapping parameters against partial genomic reference sequences that include everything except the partial cDNA sequence listed above. The table may be sorted by clicking on any column header.

Figure 5-17: Example of candidate table where the selected alleles check symbol is amber. The check symbol is colored amber in four scenarios: first, if the selected allele is not CWD; second, if the allele is inconsistent with LD information; third, if an exon of the selected allele has a mismatch with the best-matched contig; fourth, if the underlying locus is predicted to be homozygous. The blue symbol in the second column indicates there is a comment associated with the corresponding allele.

Double-click on a header to sort that column. Click on any cell to select the entire row.

Lab Technologist or Director user roles may override automatic allele calls. Common reasons for overwriting an automatic allele call include a discrepancy in phasing, incomplete detection of polymorphic sites, or presence of a novel allele.

Double-click on the second column (Call) to select or deselect an allele call (Figure 5-18). If the allele was previously checked, it will be unchecked; if the allele was unchecked, it will be checked. Each time, the activity will be logged through the comment dialog box (Figure 5-19), where a user has the option to add comments about this activity.



Figure 5-18: Check or uncheck a candidate by double-clicking on the cell in the second (Call)

column.

Figure 5-19: Comment dialog box when toggling the status of a selected allele.

Double-click on the cell in the third column (Cmt) to activate the comment dialog box (Figure 5-20). Users can log comments using the comment dialog box. This action will not change the status of the selected allele.



Figure 5-20: Comment dialog box for making comments for a selected allele.

When average coverage is less than 40X, the display shows Insufficient Data. If user wants to show genotype of a locus without sufficient data, user could type OWI (overwrite insufficient data) in a comment dialog to bring up genotypes masked by insufficient data. If user wants to cancel the effect, users could type XWI in a new comment dialog window.

Click on the tab labeled “Candidate Pair” to view the candidate pair table (Figure 5-21). In this table, each row represents a possible combination of two alleles listed in columns one and two. Eight parameters are displayed for each candidate pair. The cRead column lists the number of unique reads mapped to the cDNA reference sequences of the two alleles. The eRead column lists the number of unique reads mapped to the partial cDNA sequence containing only Exons 2 and 3 for Class I loci and only Exon 2 for Class II loci. The gRead column lists the number of unique reads mapped to the genomic reference sequences. The xRead column lists the number of unique reads mapped to partial genomic sequences that exclude the exon 2/3 regions detailed above. The mismatch numbers in the coding region and intron region for Allele1 and Allele2.



Figure 5-21: Candidate Pair table. The first and second columns (Allele1, Allele2) list the pair of alleles examined. The third column (cRead) lists the number of unique reads mapped to the cDNA reference sequences of the two alleles; the fourth column (eRead) lists the number of unique reads mapped to the partial cDNA sequence containing only Exons 2 and 3 for Class I loci and only Exon 2 for Class II loci. The fifth column (gRead) lists the number of unique reads mapped to the genomic reference sequences. The sixth column (xRead) lists the number of unique reads mapped to partial genomic sequences that exclude the exon 2/3 regions detailed above. The mismatch numbers in the coding region and intron region for Allele1 and Allele2 are located in seventh to tenth columns.

At the bottom right of candidate table, a regular expression filter box is shown for user to quickly locate alleles of interest in the candidate table (Figure 5-22)

Figure 5-22: A Regular Expression filer box is shown under candidate table.

5.5 Block E1. Coverage Plots

Coverage plots are generated for both cDNA and genomic DNA. The minimum coverage across the reference should be above 20X. For each selected reference allele in the candidate table, the coverage is plotted along either the cDNA region for the cDNA coverage plot or the genomic region for the genomic coverage plot. Overall, read



coverage is the number of individual sequence reads that were aligned across either cDNA or genomic reference sequences (Figure 5-23).

To view coverage plots, select the alleles of interest and click on the Coverage button (first button on the left).

When the sequence reads of the sample match the selected reference sequence, the coverage will be above the baseline across the entire sequenced region. When the sequence reads of the sample is mismatched to the selected reference sequence, the coverage will dip to the baseline at the mismatched position.

Positions that differ between selected reference alleles are highlighted with red bars (hash marks) above the curves. Gray shaded regions display the coverage of all alleles for the gene minus the coverage of the selected alleles. When the selected alleles map to all the reads, then there is very low amount of gray in the coverage plot. If there is a mismatch with the reference, there is clear accumulation of reads represented in grey that do not map to the reference sequence at that location.

Figure 5-23: Coverage plots. The left panel shows coverage along the cDNA reference sequences; the right panel shows coverage along the genomic reference sequences. Red bars (hash marks) above coverage curves indicate positions that are polymorphic between the selected reference alleles. Windows can be resized.

Contig block annotation track is shown in the Coverage Plot for Genomic region (Also shows in the assembly browser view). The green bars of contig block track represent homozygous regions and red bars phased heterozygous regions. The contig block sequences can be exported in the XML file (Figure 5-24).

Figure 5-24: Example of contig block annotation track under genomic Coverage Plot.



To view local alignment of selected reference sequences, hold down key and click once at the desired location within the coverage plot. Local alignment of 30 bases of the selected reference alleles will be displayed adjacent to the clicked position (Figure 5-25).

Figure 5-25: Local alignment of sequence fragment of two selected alleles at the clicked position (Shift-click). Nucleotides that differ between the selected alleles are shown in red text

The coverage plot will zoom in to a selected rectangular region as shown in Figure 5-26. To zoom, click and drag desired region within the plot. To return to the normal size coverage plot, double click anywhere within the zoomed coverage plot.

Figure 5-26: Zoomed-in view of a fraction of a coverage plot

Central coverage plots are generated for both cDNA and genomic DNA. The minimum coverage across the reference should be above 10X. For each selected reference allele in the candidate table, the central coverage is plotted along either the cDNA region for the cDNA coverage plot or the genomic region for the genomic coverage plot.

To view coverage plots, select the desired alleles for review and click on the Central button (second button on the left).

When the sequence reads of the sample match the selected reference sequence then the coverage will be above the baseline across the entire region except at exon boundaries in cDNA central coverage plot. When the sequence reads of the sample is mismatched to the selected reference sequence then the coverage will dip to the baseline at the mismatched position.



Figure 5-27: Example of central read coverage plot.

In the cDNA coverage plot, central reads are undefined at exon boundaries.

5.6 Block E2. Alignment Browsers

Click on the cDNA Browser to view alignment of reads against the cDNA reference sequence, as shown in Figure 5-28. There are several buttons for navigation. Figure 5-29 and Figure 5-30 show the zoomed-in and zoomed-out view. The first reference sequence is highlighted with gray background color and the second reference sequence is highlighted with a tan background color. Those that differ from the first and second reference sequences are highlighted in light blue and those bases are considered as noise. Hovering over a read will highlight the entire read with a cyan background color (See Figure 5-31).

Figure 5-28: Alignment browser against cDNA reference sequence. Four buttons on the top left corner allow easy navigation of the view. Prev and Next buttons jump to the previous or next polymorphic sites. Zoom In and Zoom Out buttons will zoom the display in or out. Five tracks on



the top of the alignment display coverage, length, poly site, the first and second selected alleles (if two contigs are constructed) and annotation of the reference sequence showing exon and intron locations. The red rectangle marks the cursor position where the position and the number of nucleotides are displayed in blue text.

Figure 5-29: Zoomed-in view of cDNA alignment browser.

Figure 5-30: Zoomed-out view of cDNA alignment browser. The yellow arrows are reverse reads

and the gray arrows are forward reads.



Figure 5-31: Highlight of read under cursor with cyan color in cDNA browser.

Click on Genomic Browser to view alignment of the sequence reads against the genomic reference sequence, as shown in Figure 5-32 to Figure 5-35. Genomic browser functions the same as those in the cDNA browser.

Due to large number of actual reads, only a partial set of mapped reads are plotted in the cDNA and genomic browser, while the numbers next to the red rectangle represent the total number of all mapped reads.

Figure 5-32: Alignment browser against genomic reference sequence. Four buttons on the top left corner allow easy navigation of the view. Prev and Next buttons jump to the previous or next polymorphic sites. Zoom In and Zoom Out buttons will zoom the display in or out. Five tracks on top of the alignment display coverage, length, poly site, the first and second selected alleles (if two alleles are selected) and annotation of the reference sequence showing exon and intron



locations. The red rectangle marks the cursor position where the position and number of each nucleotide are displayed in blue text.

Figure 5-33: Zoomed-in view of the genomic alignment browser.

Figure 5-34: Zoomed-out view of the genomic alignment browser.



Figure 5-35: Individual reads under the cursor are highlighted in cyan in the genomic browser. The

deletion is indicated by missing bases in the reference sequence.

5.7 Block E3. Reference Alignment

Click on the Reference Alignment button to view the reference sequence alignment of selected alleles as shown in Figure 5-36 (for cDNA reference sequences) and in Figure 5-37 (for genomic reference sequences).



Figure 5-36: cDNA reference alignment of selected references in the candidate table. Pink bars delineate the exon boundaries. Positions different among selected alleles are highlighted.



Figure 5-37: Genomic reference alignment of selected references in the candidate table. Pink bars delineate the intron-exon boundaries. Positions that differ among the selected alleles are highlighted.



Click on the Consensus Alignment button to display multiple sequence alignment between selected alleles and their best-matched de novo assembled contig (Figure 5-38 to Figure 5-41). The Phased Resolved Consensus sequence can be aligned with any selected candidate alleles by clicking on the Consensus Alignment browser. The alignment between selected alleles from the candidate table and the best-matched contig will display. The best-matched contig is defined as the least number of exon mismatches.

Figure 5-38: Multiple alignment of selected cDNA reference sequences with de novo contig1 sequence. Positions that differ between contig1 and the reference sequence are highlighted. When no sequence is available the position is denoted with N.



Figure 5-39: Multiple sequence alignment of selected cDNA reference sequences with de novo contig2 sequence. Positions that differ between contig2 and the reference sequence are highlighted.



Figure 5-40: Multiple alignment of selected genomic reference sequences with de novo contig1 sequence. Positions different between contig1 sequence and reference sequences are highlighted. Exon regions are shaded in gray.



Figure 5-41: Multiple alignment of selected genomic reference sequences with de novo contig2 sequence. Positions different between contig2 sequence and reference sequences are highlighted. Exon regions are shaded in gray.



Click on the Clear button to de-select any selected alleles in the candidate table.

The amino acid coding can be displayed by double- clicking on any nucleotide, especially for mismatch(s) in the alignment, a user can easily determine whether the codon change is non-synonymous (Figure 5-42).

Figure 5-42: Display amino acid coding in the contig sequence alignment window.

To navigate to specific base position in the contig alignment browser, a user can push and hold shift and double-click on the nucleotide in the cDNA or genomic alignment, then the corresponding position in the contig alignment browser will be centered. A red vertical box will highlight the selected position across the contig sequences and assembled reads that covering the position (Figure 5-43).

Figure 5-43: Contig Alignment Browser centered at the select base position from contig sequence

alignment.

5.8 Regular Expression Filter

The Regular Expression Filter provides a search tool for allele of interest in the Candidate allele table as well as Candidate Pair Table. For instance, if the user is looking for null alleles, “N” can be typed in the Regular Expression Filter box. All the null alleles associated with the selected locus are going to be listed both in the candidate allele table as well as candidate pair table.



Figure 5-44: Regular Expression Filter facilitates searching for the specific allele.

5.9 Block F. Contig Alignment Browser The contig alignment browser (Figure 5-45 to Figure 5-42) displays the de novo assembled contigs for the selected sample. The functions in the browser are similar to the genomic alignment browser (Figure 5-32) shown above. In addition, the contig alignment browser has an “Export Consensus” button allowing the user to export the contigs in fasta format.

Figure 5-45: Contig alignment browser showing reads mapped to de novo contigs. Five buttons on the top left corner allow easy navigation of the view. Prev and Next buttons jump to the



previous or next polymorphic sites. Zoom In and Zoom Out buttons will zoom the display in or out. The Export Consensus button allows exporting the contig (consensus) sequences. Five tracks on top of the alignment display coverage, length, poly site, the first and second selected alleles (if two contigs are constructed) and annotation of the reference sequence showing exon and intron locations. The red rectangle marks the cursor position where the position and the number of each nucleotide are displayed in blue text.

Figure 5-46: Zoomed-in view of contig browser showing reads mapped onto de novo contigs.

Figure 5-47: Zoomed-out view of contig browser showing reads mapped onto de novo contigs. Gray rectangles denote paired-end reads; yellow arrows denote non-paired-end reads (singletons).



Figure 5-48: Contig Browser showing a single read highlighted in cyan; highlighting is visible by

hovering the mouse over the display.

The unsequenced region between the paired-end reads is indicated with N. 5.9.1 Interaction between Variants table and contig alignment browser Click on any position in the first column (Pos) of the Variants table to highlight the polymorphic site in the contig alignment browser as shown in Figure 5-49.

Figure 5-49: Interaction between Variants table and contig alignment browser; clicking on a position in the first column (Pos) of the Variants table will center the Variants in the contig alignment browser.

5.9.2 Interaction between cDNA browser and contig alignment browser Click on a location in the cDNA browser to center it in the contig alignment browser as shown in Figure 5-50.



Figure 5-50: Interaction between cDNA alignment browser and contig alignment browser. Click on a position in cDNA browser to center the corresponding position in the contig alignment browser. The opposite action is also possible (click on contig browser to center the cDNA browser).

5.9.3 Interaction between genomic browser and contig alignment browser Click on a location in the genomic browser to center the contig alignment browser at the corresponding position as shown in Figure 5-51.

Figure 5-51: Interaction between genomic alignment browser and contig alignment browser. Click on a position in genomic browser to center the corresponding position in the contig alignment browser. The opposite action is also possible (click on contig browser to center the genomic browser).



5.9.4 Interaction between genomic coverage plot and contig alignment browser

Hold down key and double click on a position in the genomic coverage plot to center the contig alignment browser at the corresponding position (Figure 5-52).

Figure 5-52: Interaction between genomic coverage plot and contig alignment browser. Hold down key and double-click on a position in genomic coverage plot to center the corresponding position in the contig browser.

5.9.5 Interaction between cDNA coverage plot and contig alignment browser Hold down key and double-click on a position in cDNA coverage plot to center the contig browser at the corresponding position (Figure 5-53).



Figure 5-53: Interaction between cDNA coverage plot and contig alignment browser. Hold down key and double-click on a position in cDNA coverage plot to center the corresponding position in the contig browser.

5.9.6 Interaction between cDNA coverage plot and cDNA alignment browser Hold down key and double-click on a position in the cDNA coverage plot to open a new tab displaying the cDNA Alignment Browser and centered at the corresponding position (Figure 5-54).



Figure 5-54: Interaction between the cDNA coverage plot and the cDNA browse

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