15 | | * Is your sequencing library stranded or unstranded? This information is needed to help these tools accurately count features. Strandedness of some library prep methods: |
16 | | * TruSeq Stranded mRNA Kits ("TruSeqStrandedPolyA") reads are reverse stranded (stranded in the reverse direction relative to the transcript orientation). |
17 | | * SMART-Seq v4 Ultra Low Input RNA Kit ("SMARTerUltra-lowPOLYA-V4") reads are unstranded. |
18 | | * KAPA RNA HyperPrep Kits ("KAPAHyperPrepmRNA") reads are reverse stranded. |
19 | | * The Whitehead Genome Core has some more [http://genomecore.wi.mit.edu/index.php/NCBISubmission Library Prep Descriptions]. |
20 | | * See [[SAMBAMqc]] (and/or look at mapped reads in a genome browser) to determine or verify strandedness |
| 15 | {{{ |
| 16 | # Unstranded reads |
| 17 | TEtranscripts --format BAM --stranded no -t treat1.bam treat2.bam -c control1.bam control2.bam --GTF genes.gtf --TE transposons.gtf --mode multi --project treat_vs_control --minread 1 -i 100 --padj 0.05 --norm DESeq_default --sortByPos |
30 | | |
31 | | # paired-end reads (unstranded) |
32 | | featureCounts -p -a gene_anotations.gtf -o MySample.featureCounts.txt MySample.bam |
33 | | # paired-end reads (forward stranded) |
34 | | featureCounts -p -s 1 -a gene_annotations.gtf -o MySamples.featureCounts.txt *sortedByName.bam |
35 | | # paired-end reads (reverse stranded) |
36 | | featureCounts -p -s 2 -a gene_annotations.gtf -o MySamples.featureCounts.txt *sortedByName.bam |
| 22 | # Reverse stranded reads |
| 23 | TEtranscripts --format BAM --stranded reverse -t treat1.bam treat2.bam -c control1.bam control2.bam --GTF genes.gtf --TE transposons.gtf --mode multi --project treat_vs_control --minread 1 -i 100 --padj 0.05 --norm DESeq_default --sortByPos |
41 | | * Review articles: |
42 | | * [[http://www.ncbi.nlm.nih.gov/pubmed/24020486|Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.]] - Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D. Genome Biol. 2013 Sep 10;14(9):R95. |
43 | | * [[http://www.ncbi.nlm.nih.gov/pubmed/21106489|A survey of statistical software for analyzing RNA-seq data]] - Gao D, Kim J, Kim H, Phang TL, Selby H, Tan AC, Tong T. Hum Genomics. 2010 Oct;5(1):56-60. |
44 | | * [[http://www.ncbi.nlm.nih.gov/pubmed/21176179|From RNA-seq reads to differential expression results]] - Oshlack A, Robinson MD, Young MD. Genome Biol. 2010;11(12):220. Epub 2010 Dec 22. |
45 | | * For more practical information, see the third session of [http://jura.wi.mit.edu/bio/education/R2011/ An introduction to R and Bioconductor: A BaRC Short Course] and the [http://jura.wi.mit.edu/bio/hot_topics/ BaRC Hot Topic] (under "Short Read Sequencing", see "Practical RNA-Seq analysis") |
| 28 | * Alternative software: |
| 29 | * [[https://www.nature.com/articles/s41467-019-13035-2|Transposable element expression in tumors is associated with immune infiltration and increased antigenicity]] - Yu Kong, Christopher M. Rose, Ashley A. Cass, Alexander G. Williams, Martine Darwish, Steve Lianoglou, Peter M. Haverty, Ann-Jay Tong, Craig Blanchette, Matthew L. Albert, Ira Mellman, Richard Bourgon, John Greally, Suchit Jhunjhunwala & Haiyin Chen-Harris ''Nature Communications'' '''10''', 5228 (2019) |