Citations¶
https://github.com/bcbio/bcbio-nextgen
small RNA-seq¶
Data was analyzed with bcbio-nextgen (https://github.com/bcbio/bcbio-nextgen) using piDNA to detect the adapter, cutadapt to remove it, STAR/bowtie to align against the genome and seqcluster to detect small RNA transcripts. miRNAs were detected using miraligner tool with miRBase as the reference miRNA database. tRNA profiles were detected using tdrmapper tool. mirdeep2 was used for discovery of novel miRNAs. FastQC was used for QC metrics and multiqc for reporting.
Download BIB format: https://github.com/bcbio/bcbio-nextgen/tree/master/docs/contents/misc/bcbio-smallrna.bib
Tools¶
Tsuji J, Weng Z. (2016) DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data. 11(10):e0164228. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164228
Andrews, S. (2010). FastQC: A quality control tool for high throughput sequence data. Bioinformatics. doi:citeulike-article-id:11583827
Didion, J. P., Martin, M., & Collins, F. S. (2017). Atropos: specific, sensitive, and speedy trimming of sequencing reads. http://doi.org/10.7287/peerj.preprints.2452v4
Dale, R. K., Pedersen, B. S., & Quinlan, A. R. (2011). Pybedtools: A flexible Python library for manipulating genomic datasets and annotations. Bioinformatics, 27(24), 3423–3424. doi:10.1093/bioinformatics/btr539
Quinlan, A. R., & Hall, I. M. (2010). BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), 841–842. doi:10.1093/bioinformatics/btq033
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J., & Prins, P. (2015). Sambamba: Fast processing of NGS alignment formats. Bioinformatics, 31(12), 2032–2034. doi:10.1093/bioinformatics/btv098
Heger, A. (2009). Pysam. github.com. Retrieved from https://github.com/pysam-developers/pysam
Li, H. (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987–2993. doi:10.1093/bioinformatics/btr509
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. doi:10.1093/bioinformatics/btp352
Pantano, L., Estivill, X., & Martí, E. (2010). SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Research, 38(5), e34. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20008100
Pantano, L., Friedlander, M. R., Escaramis, G., Lizano, E., Pallares-Albanell, J., Ferrer, I., … Marti, E. (2015). Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson’s disease revealed by deep sequencing analysis. Bioinformatics (Oxford, England). doi:10.1093/bioinformatics/btv632
For the alignment, add what you have used:
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., … Gingeras, T. R. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21. doi:10.1093/bioinformatics/bts635
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10, R25. doi:10.1186/gb-2009-10-3-r25
Kim, D., Langmead, B. & Salzberg, SL. (2016). HISAT: a fast spliced aligner with low memory requirements. Nature Methods, 12(4): 357–360. doi: 10.1038/nmeth.3317
If you used TopHat2 for alignment:
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(4): R36. doi: 10.1186/gb-2013-14-4-r36
Brueffer, C. & Saal, LH. (2016). TopHat-Recondition: A post-processor for TopHat unmapped reads. BMC Bioinformatics, 17(1):199. doi: 10.1186/s12859-016-1058-x
If you have in the output novel miRNA discovering, add:
Friedlander, M. R., MacKowiak, S. D., Li, N., Chen, W., & Rajewsky, N. (2012). MiRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Research, 40(1), 37–52. doi:10.1093/nar/gkr688
If you have tRNA mapping output, add:
Selitsky, S. R., & Sethupathy, P. (2015). tDRmapper: challenges and solutions to mapping, naming, and quantifying tRNA-derived RNAs from human small RNA-sequencing data. BMC Bioinformatics, 16(1), 354. doi:10.1186/s12859-015-0800-0
If you have miRge activated:
Yin Lu, Alexander S. Baras, Marc K Halushka. miRge2.0: An updated tool to comprehensively analyze microRNA sequencing data. bioRxiv.org.
If you have MINTmap activated:
Loher, P, Telonis, AG, Rigoutsos, I. MINTmap: fast and exhaustive profiling of nuclear and mitochondrial tRNA fragments from short RNA-seq data. Sci Rep. 2017;7 :41184. doi: 10.1038/srep41184. PubMed PMID:28220888 PubMed Central PMC5318995.
Data¶
Griffiths-Jones, S. (2004). The microRNA Registry. Nucleic Acids Research, 32(Database issue), D109–11. doi:10.1093/nar/gkh023
Griffiths-Jones, S. (2006). miRBase: the microRNA sequence database. Methods in Molecular Biology (Clifton, N.J.), 342, 129–38. doi:10.1385/1-59745-123-1:129
Griffiths-Jones, S., Saini, H. K., Van Dongen, S., & Enright, A. J. (2008). miRBase: Tools for microRNA genomics. Nucleic Acids Research, 36(SUPPL. 1). doi:10.1093/nar/gkm952
Kozomara, A., & Griffiths-Jones, S. (2011). MiRBase: Integrating microRNA annotation and deep-sequencing data. Nucleic Acids Research, 39(SUPPL. 1). doi:10.1093/nar/gkq1027
Kozomara, A., & Griffiths-Jones, S. (2014). MiRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 42(D1). doi:10.1093/nar/gkt1181