RNA-Seq Narrative for Adler et al., 2020
Benjamin Adler, Adam Arkin, Elisha WC, Ben Allen, Zach Crockett
Generated May 7, 2020

Supplemental RNA-Seq Processing Notebook for:

Systematic Discovery of Salmonella Phage-Host Interactions via High-Throughput Genome-Wide Screens

Benjamin A. Adler, Crystal Zhong, Hualan Liu, Elizabeth Kutter, Adam M. Deutschbauer, Vivek K. Mutalik, Adam P. Arkin

DOI: https://doi.org/10.1101/2020.04.27.058388

This KBase narrative contains RNA-Seq data processing for Adler et al., "Systematic Discovery of Salmonella Phage-Host Interactions via High-Throughput Genome-Wide Screens", 2020. The goal of this experiment was to identify shared transcription-level difference between phage cross-resistant mutants, ∆trkH, ∆sapB, ∆rpoN, ∆himA relative to wild-type S. typhimurium MS1868. In this narrative, we processed RNA-Seq reads into differential expression analysis datasets. We began from pre-loaded RNA-Seq reads (PairedEndLibrary objects) and output datasets available in Supplementary Datasets 5 and 6 (doi.org/10.6084/m9.figshare.12185031).

Process Overview

Before the narrative begins, upload RNA-Seq datasets to KBase as PairedEndLibrary Objects. Sequencing results are the product of HiSeq4000 sequencing using 100PE runs (see Methods).

  1. Upload Reference Genome

    Upload a suitable, annotated reference genome serving as the basis for RNA-Seq alignment (S. typhimurium LT2 genome and PSLT plasmids). These are based off of RefSeq accession numbers NC_003197.2 and NC_003277.2 respectively.

  2. Trim reads

    Trim RNA-Seq reads with Trimmomatic for each PairedEndLibrary. This step removes technical sequences such as indexes using in Illumina sequencing as well as removing reads of insufficient quality.

  3. Group reads

    Establish RNA-Seq Sample Set. This step groups PairedEndLibraries by grouping (see Sample Nomenclature). From hereon out, HiSAT Alignment and DE-Seq differential expression can be performed using this object to simplify the process.

  4. Align reads

    Align trimmed RNA-Seq reads to the LT2 + PSLT reference genome using HISAT2. This step provides position-level coverage for each sample.

  5. Assemble Transcripts

    Assemble transcripts based off of the HISAT2 alignments using StringTie. This step provides gene-annotation-level coverage for each sample and creates the output seen in Supplementary Dataset 5. Because sample processing as described in (Methods) yielded larger fragments than most sRNA transcripts, sRNAs abundances are primarily depleted.

  6. Differential expression

    Perform differential expression calculations using DESeq2. This step calculates gene-annotation expression-level differences by condition. Of note, this step calculates the all-by-all differential expression matrix, but only differential expression against wild-type was used. This creates the output seen in Supplementary Dataset 6.

Sample Nomenclature

Biological triplicate RNA-Seq experiments of:

  • A1, A2, A3: Wild-type S. typhimurium MS1868
  • B1, B2, B3: S. typhimurium MS1868 ∆trkH
  • C1, C2, C3: S. typhimurium MS1868 ∆sapB
  • D1, D2, D3: S. typhimurium MS1868 ∆rpoN
  • E1, E2: S. typhimurium MS1868 ∆himA (∆ihfA)

References:

    Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Lucchini, S., McDermott, P., Thompson, A. & Hinton, J. C. D. The H-NS-like protein StpA represses the RpoS (sigma 38) regulon during exponential growth of Salmonella Typhimurium. Mol. Microbiol. 74, 1169–1186 (2009).

    Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).

1. Upload Reference Genome

Uploaded a suitable, annotated reference genome serving as the basis for RNA-Seq alignment (S. typhimurium LT2 genome and PSLT plasmids). These are based off of RefSeq accession numbers NC_003197.2 and NC_003277.2, respectively. Of note, a key difference between the reference genome NC_003197.2 and the strain used, S. typhimurium MS1868 is that MS1868 does not have the prophage Fels2. As such, we expect a drop in alignment quality from nucleotides 2844431 - 2879237.

Inputs

Outputs

Genomes that will be used as a reference during HISAT2 Alignment (Step 4) and onwards.

Import GFF3/FASTA file as Genome from Staging Area
Import a GFF3 and FASTA file from your staging area into your Narrative as a Genome data object
This app completed without errors in 1m 37s.
Objects
Created Object Name Type Description
LT2_gff3.gff_genome Genome Imported Genome
Report
Links
  • report.html - HTML summary report for imported Genome
Output from Import GFF3/FASTA file as Genome from Staging Area
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/48675

2. Trim reads

Trim RNA-Seq reads with Trimmomatic for each PairedEndLibrary. This step removes technical sequences such as indexes using in Illumina sequencing as well as removing reads of insufficient quality. TruSeq3-PE adapters will be removed. This step is performed for each PairedEndLibrary, so this process was performed 14 times, once for each library.

Input

  • PairedEndLibrary sequencing reads corresponding to RNA-Seq samples uploaded before this pipeline began.

Output

  • Trimmed output paired end library with reads trimmed and/or removed. Thus all reads are theoretically of high technical quality and likely correspond to biological sample.
  • Report on trimming output - how many reads were trimmed and discarded.

Analysis

Each report refers to a single sequenced sample. This gives insight into how many reads were removed from further processing. Given that most reads were retained, no action was needed.

Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 28m 51s.
Objects
Created Object Name Type Description
BA_A1_trimmed_paired PairedEndLibrary Trimmed Reads
BA_A1_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_A1_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 24m 21s.
Objects
Created Object Name Type Description
BA_A2_trimmed_paired PairedEndLibrary Trimmed Reads
BA_A2_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_A2_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 55m 16s.
Objects
Created Object Name Type Description
BA_A3_trimmed_paired PairedEndLibrary Trimmed Reads
BA_A3_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_A3_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 54m 23s.
Objects
Created Object Name Type Description
BA_B1_trimmed_paired PairedEndLibrary Trimmed Reads
BA_B1_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_B1_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 55m 31s.
Objects
Created Object Name Type Description
BA_B2_trimmed_paired PairedEndLibrary Trimmed Reads
BA_B2_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_B2_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 1h 11m 8s.
Objects
Created Object Name Type Description
BA_B3_trimmed_paired PairedEndLibrary Trimmed Reads
BA_B3_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_B3_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 24m 7s.
Objects
Created Object Name Type Description
BA_C1_trimmed_paired PairedEndLibrary Trimmed Reads
BA_C1_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_C1_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 16m 52s.
Objects
Created Object Name Type Description
BA_C2_trimmed_paired PairedEndLibrary Trimmed Reads
BA_C2_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_C2_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 24m 57s.
Objects
Created Object Name Type Description
BA_C3_trimmed_paired PairedEndLibrary Trimmed Reads
BA_C3_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_C3_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 24m 40s.
Objects
Created Object Name Type Description
BA_D1_trimmed_paired PairedEndLibrary Trimmed Reads
BA_D1_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_D1_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 58m 54s.
Objects
Created Object Name Type Description
BA_D2_trimmed_paired PairedEndLibrary Trimmed Reads
BA_D2_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_D2_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 23m 58s.
Objects
Created Object Name Type Description
BA_D3_TRIMMED_paired PairedEndLibrary Trimmed Reads
BA_D3_TRIMMED_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_D3_TRIMMED_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 43m 43s.
Objects
Created Object Name Type Description
BA_E1_trimmed_paired PairedEndLibrary Trimmed Reads
BA_E1_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_E1_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links
Trim Reads with Trimmomatic - v0.36
Trim paired- or single-end Illumina reads with Trimmomatic.
This app completed without errors in 23m 41s.
Objects
Created Object Name Type Description
BA_E2_trimmed_paired PairedEndLibrary Trimmed Reads
BA_E2_trimmed_unpaired_fwd SingleEndLibrary Trimmed Unpaired Forward Reads
BA_E2_trimmed_unpaired_rev SingleEndLibrary Trimmed Unpaired Reverse Reads
Report
Links

3. Group reads

Establish RNA-Seq Sample Set. This step groups PairedEndLibraries by grouping (see Sample Nomenclature). From here on out, HiSAT Alignment and DE-Seq differential expression can be performed using this object to simplify the process.

Input

  • PairedEndLibrary sequencing reads corresponding to RNA-Seq samples uploaded before this pipeline began.
  • Grouping information determined by the experimentalist. In this case, grouping is by genetic mutant of S. typhimurium MS1868

Output

  • RNA-Seq Sample Set. This will be used to process all samples together in Steps 4-6.
Create RNA-seq Sample Set
Allows users to provide RNA-seq reads and the corresponding metadata to create an RNASeqSampleSet data object.
This app completed without errors in 6s.
No output found.
Output from Create RNA-seq Sample Set
The viewer for the output created by this App is available at the original Narrative here: https://narrative.kbase.us/narrative/48675

4. Align reads

Align trimmed RNA-Seq reads to the LT2 + PSLT reference genome using HISAT2. This step provides position-level coverage for each sample.

Inputs

  • RNA-Seq Sample Set from Step 3 (allows automation of HISAT2 across all samples).
  • LT2_gff3.gff_genome: Reference genome as imported in Step 1.

Outputs

  • HISAT2 Alignment Sample Set (allows automation of StringTie across all samples). There are also 14 HISAT2 alignments that could be investigated individually if desired.
  • QualiMap report - Describes the quality of alignment across each sample.

Brief Analysis (from the QualiMap Report)

  • We are generally happy with the alignment to S. typhimurium LT2 even though this is not the exact strain background.
  • All samples had high mean mapping qualities with high coverage (max=60).
  • As expected, all samples had median insert sizes ~200bp, longer than most sRNAs. Thus, as predicted, most sRNAs were likely not captured during RNA-Seq sample preparation.
  • Broadly speaking, samples across groupings could be discriminated by PCA, indicating differences in expression as grouped by genotype.
  • Some regions have exceptionally high coverage across the reference. We believe this is due to rRNA contamination. As described in Methods, due to non-intact rRNA we could not deplete all likely rRNA during sample preparation.
  • As seen in the mapping quality across the reference graph, the LT2 reference was a generally good reference except for a couple regions. These regions correspond to regions with expected decreases in mapping quality. For instance, most of these regions correspond to repetitive regions (such as rDNA). In addition, the region ~0.57-0.58 corresponds to Fels2, which is expectedly not in the strain background, but is in the reference.
  • The expected absence of Fels2 is further confirmed by the gap around the region ~0.57-0.58 in all samples.
Align Reads using HISAT2 - v2.1.0
Align sequencing reads to long reference sequences using HISAT2.
This app completed without errors in 7h 19m 22s.
Objects
Created Object Name Type Description
BA_B2_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/31/1 aligned to Genome 48675/69/1
BA_E1_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/73/2 aligned to Genome 48675/69/1
BA_A2_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/16/1 aligned to Genome 48675/69/1
BA_A3_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/30/1 aligned to Genome 48675/69/1
BA_C1_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/28/1 aligned to Genome 48675/69/1
BA_D1_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/50/1 aligned to Genome 48675/69/1
BA_C2_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/44/1 aligned to Genome 48675/69/1
BA_B1_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/25/1 aligned to Genome 48675/69/1
BA_D3_TRIMMED_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/57/1 aligned to Genome 48675/69/1
BA_C3_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/49/1 aligned to Genome 48675/69/1
BA_A1_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/20/1 aligned to Genome 48675/69/1
BA_B3_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/24/2 aligned to Genome 48675/69/1
BA_D2_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/66/2 aligned to Genome 48675/69/1
BA_E2_trimmed_paired_alignment_trimmed RNASeqAlignment Reads 48675/79/1;48675/62/1 aligned to Genome 48675/69/1
SE_reprocessed_trimmed_alignment_set_trimmed ReadsAlignmentSet Set of all new alignments
Report
Summary
Created 14 alignments from the given alignment set.
Links

5. Assemble Transcripts

Assemble transcripts based off of the HISAT2 alignments and S. typhimurium LT2 annotations using StringTie. This step provides gene-annotation-level coverage for each sample and creates the output seen in Supplementary Dataset 5. Because sample processing as described in Methods yielded larger fragments than most sRNA transcripts, sRNAs abundances are primarily depleted, but could be mapped if the sRNA were sufficiently long.

Inputs

  • HISAT2 Alignment Sample Set (allows automation of StringTie across all samples) (from Step 4). There are also 14 HISAT2 alignments that could be investigated individually if desired. This input object also contains references to LT2_gff3.gff_genome, the reference genome as imported in Step 1.

Outputs

  • StringTie ExpressionSet (allows automation of DESeq2 and heatmap visualization of expression levels across all samples). There are also 14 StringTie assembled transcripts that could be investigated individually if desired. This output object is seen in Supplementary Dataset 5. An example of the heatmap visualization is included.

Analysis

Qualitative analysis could be performed by looking at the interactive heatmap below (TPM). For instance in ∆sapB experiments (samples B1, B2, B3), sapB (STM1693) expression levels are expectedly lower relative to wild-type MS1868 (samples A1, A2, A3). Same for trkH (STM3986) (samples C1, C2, C3), rpoN (STM3320) (samples D1, D2, D3), and himA (STM1339) (samples E1, E2). For differential expression analysis, normalization and hypothesis testing using the DESeq2 output should be employed.

Assemble Transcripts using StringTie - v1.3.3b
Assemble the transcripts from RNA-seq reads using StringTie
This app completed without errors in 1h 18m 34s.
Objects
Created Object Name Type Description
SE_reprocessed_trimmed_expression_set_trimmed_trimmed ExpressionSet ExpressionSet generated by StringTie
BA_A1_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_A2_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_A3_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_B1_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_B2_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_B3_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_C1_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_C2_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_C3_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_D1_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_D2_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_D3_TRIMMED_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_E1_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
BA_E2_trimmed_paired_expression_trimmed_trimmed RNASeqExpression Expression generated by StringTie
SE_reprocessed_trimmed_trimmed_trimmed_FPKM_ExpressionMatrix ExpressionMatrix FPKM ExpressionMatrix generated by StringTie
SE_reprocessed_trimmed_trimmed_trimmed_TPM_ExpressionMatrix ExpressionMatrix TPM ExpressionMatrix generated by StringTie
Report
Links
Files
These are only available in the live Narrative: https://narrative.kbase.us/narrative/48675
  • stringtie_result.zip - File(s) generated by StringTie App 
from biokbase.narrative.jobs.appmanager import AppManager
AppManager().run_local_app(
    "NarrativeViewers/view_expression_interactive_heatmap",
    {
    "param0": "SE_reprocessed_trimmed_trimmed_trimmed_TPM_ExpressionMatrix"
},
    tag="release",
    version="1.0.7",
    cell_id="b6167e7e-f7a8-46f5-809b-6336b6e4e57e",
    run_id="ed274591-87ec-4e0c-8ab7-45d7ee23acbd"
)
Out[8]:

6. Differential expression

Perform differential expression calculations using DESeq2. This step calculates gene-annotation expression-level differences by condition. Of note, this step calculates the all-by-all differential expression matrix, but only differential expression against wild-type was used. This creates the output seen in Supplementary Dataset 6.

Inputs

  • StringTie ExpressionSet (links to all individual StringTie outputs) (from Step 5). Because DESeq2 estimates a negative binomial distribution to perform hypothesis testing between gene features, replicates are needed. DESeq2 should not be run on samples with fewer replicates than 2 and is advised for more than 3.

Outputs

  • DESeq2 Differential Expression Matrixes: Matrixes showing the estimated log2-fold-change of DESeq2 counts, estimated dispersion, and statistical differences are shown. A filtered version of these tables are shown in Supplementary Dataset 6.

Analysis

  • High-level expression level differences can be inferred in the attached PCA output.
  • Further processing of this data included calculating a more conservative multiple-hypothesis-testing-correction metric (Bonferoni) and filtering for large-magnitude expression level and statistical differences. Analysis of this data were used to determine that RpoS-regulated genes were at higher levels in ∆trkH, ∆sapB, and ∆rpoN strain backgrounds. RpoS-regulated genes were defined as those from Lucchini et al.,2009 (Supplementary Table 1).
Create Differential Expression Matrix using DESeq2 - v1.20.0
Create differential expression matrix based on a given threshold cutoff
This app is still in progress.
No output found.

Apps

  1. Align Reads using HISAT2 - v2.1.0
    • Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature Methods. 2015;12: 357 360. doi:10.1038/nmeth.3317
    • Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology. 2013;14: R36. doi:10.1186/gb-2013-14-4-r36
  2. Assemble Transcripts using StringTie - v1.3.3b
    • Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT & Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nature Biotechnology 2015
    • Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology. 14:R36
    • Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter, L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7(3), 562 578.
    • Trapnell C, Pachter L, Salzberg SL. (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. Vol 25, 9:1105-1111.
    • Frazee, A. C., Pertea, G., Jaffe, A. E., Langmead, B., Salzberg, S. L., & Leek, J. T. (2015). Ballgown bridges the gap between transcriptome assembly and expression analysis. Nature Biotechnology, 33(3), 243 246.
  3. Create Differential Expression Matrix using DESeq2 - v1.20.0
    • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15: 550. doi:10.1186/s13059-014-0550-8
  4. Create RNA-seq Sample Set
    • Arkin AP, Cottingham RW, Henry CS, Harris NL, Stevens RL, Maslov S, et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nature Biotechnology. 2018;36: 566. doi: 10.1038/nbt.4163
  5. Import GFF3/FASTA file as Genome from Staging Area
    no citations
  6. Trim Reads with Trimmomatic - v0.36
    • Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114 2120. doi:10.1093/bioinformatics/btu170