@misc{Andrews2010,
abstract = {FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming from high throughput sequencing pipelines. It provides a modular set of analyses which you can use to give a quick impression of whether your data has any problems of which you should be aware before doing any further analysis. The main functions of FastQC are Import of data from BAM, SAM or FastQ files (any variant) Providing a quick overview to tell you in which areas there may be problems Summary graphs and tables to quickly assess your data Export of results to an HTML based permanent report Offline operation to allow automated generation of reports without running the interactive application},
author = {Andrews, S.},
booktitle = {Bioinformatics},
doi = {citeulike-article-id:11583827},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
pages = {1},
title = {{FastQC: A quality control tool for high throughput sequence data}},
url = {http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:FastQC+a+quality+control+tool+for+high+throughput+sequence+data.{\#}0},
year = {2010}
}
@article{Griffiths-Jones2004,
abstract = {The miRNA Registry provides a service for the assignment of miRNA gene names prior to publication. A comprehensive and searchable database of published miRNA sequences is accessible via a web interface (http://www.sanger.ac.uk/Software/Rfam/mirna/), and all sequence and annotation data are freely available for download. Release 2.0 of the database contains 506 miRNA entries from six organisms.},
author = {Griffiths-Jones, Sam},
doi = {10.1093/nar/gkh023},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {1362-4962},
journal = {Nucleic acids research},
keywords = {Animals,Base Sequence,Computational Biology,Databases,Humans,Internet,MicroRNAs,MicroRNAs: chemistry,MicroRNAs: genetics,Nucleic Acid,Nucleic Acid Conformation,Registries,bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {Database issue},
pages = {D109--11},
pmid = {14681370},
title = {{The microRNA Registry.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/14681370},
volume = {32},
year = {2004}
}
@article{Griffiths-Jones2006,
abstract = {The miRBase Sequence database is the primary repository for published microRNA (miRNA) sequence and annotation data. miRBase provides a user-friendly web interface for miRNA data, allowing the user to search using key words or sequences, trace links to the primary literature referencing the miRNA discoveries, analyze genomic coordinates and context, and mine relationships between miRNA sequences. miRBase also provides a confidential gene-naming service, assigning official miRNA names to novel genes before their publication. The methods outlined in this chapter describe these functions. miRBase is freely available to all at http://microrna.sanger.ac.uk/.},
author = {Griffiths-Jones, Sam},
doi = {10.1385/1-59745-123-1:129},
isbn = {1064-3745 (Print)},
issn = {1064-3745},
journal = {Methods in Molecular Biology (Clifton, N.J.)},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
pages = {129--38},
pmid = {16957372},
title = {{miRBase: the microRNA sequence database}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16957372$\backslash$nhttp://www.ncbi.nlm.nih.gov/pubmed/16957372?ordinalpos=2{\&}itool=EntrezSystem2.PEntrez.Pubmed.Pubmed{\_}ResultsPanel.Pubmed{\_}DefaultReportPanel.Pubmed{\_}RVDocSum},
volume = {342},
year = {2006}
}
@article{Griffiths-Jones2008,
abstract = {miRBase is the central online repository for microRNA (miRNA) nomenclature, sequence data, annotation and target prediction. The current release (10.0) contains 5071 miRNA loci from 58 species, expressing 5922 distinct mature miRNA sequences: a growth of over 2000 sequences in the past 2 years. miRBase provides a range of data to facilitate studies of miRNA genomics: all miRNAs are mapped to their genomic coordinates. Clusters of miRNA sequences in the genome are highlighted, and can be defined and retrieved with any inter-miRNA distance. The overlap of miRNA sequences with annotated transcripts, both protein- and non-coding, are described. Finally, graphical views of the locations of a wide range of genomic features in model organisms allow for the first time the prediction of the likely boundaries of many miRNA primary transcripts. miRBase is available at http://microrna.sanger.ac.uk/.},
author = {Griffiths-Jones, Sam and Saini, Harpreet Kaur and {Van Dongen}, Stijn and Enright, Anton J.},
doi = {10.1093/nar/gkm952},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {03051048},
journal = {Nucleic Acids Research},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {SUPPL. 1},
pmid = {17991681},
title = {{miRBase: Tools for microRNA genomics}},
volume = {36},
year = {2008}
}
@article{Kozomara2011,
abstract = {miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Kozomara, Ana and Griffiths-Jones, Sam},
doi = {10.1093/nar/gkq1027},
eprint = {NIHMS150003},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {03051048},
journal = {Nucleic Acids Research},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {SUPPL. 1},
pmid = {21037258},
title = {{MiRBase: Integrating microRNA annotation and deep-sequencing data}},
volume = {39},
year = {2011}
}
@article{Kozomara2014,
abstract = {We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.},
author = {Kozomara, Ana and Griffiths-Jones, Sam},
doi = {10.1093/nar/gkt1181},
isbn = {1362-4962 (Electronic)},
issn = {03051048},
journal = {Nucleic Acids Research},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {D1},
pmid = {24275495},
title = {{MiRBase: Annotating high confidence microRNAs using deep sequencing data}},
volume = {42},
year = {2014}
}
@misc{Heger2009,
abstract = {Pysam is a python module for reading and manipulating Samfiles. It's a lightweight wrapper of the samtools C-API. Pysam also includes an interface for tabix.},
author = {Heger, Andreas},
booktitle = {github.com},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
title = {{Pysam}},
url = {https://github.com/pysam-developers/pysam},
year = {2009}
}
@article{Quinlan2010,
abstract = {MOTIVATION: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.$\backslash$n$\backslash$nRESULTS: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets.$\backslash$n$\backslash$nAVAILABILITY AND IMPLEMENTATION: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools$\backslash$n$\backslash$nCONTACT: aaronquinlan@gmail.com; imh4y@virginia.edu$\backslash$n$\backslash$nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
author = {Quinlan, Aaron R. and Hall, Ira M.},
doi = {10.1093/bioinformatics/btq033},
isbn = {1367-4811 (Electronic)$\backslash$n1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {6},
pages = {841--842},
pmid = {20110278},
title = {{BEDTools: A flexible suite of utilities for comparing genomic features}},
volume = {26},
year = {2010}
}
@article{Dale2011,
abstract = {SUMMARY: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats. It provides an intuitive Python interface that extends upon the popular BEDTools genome arithmetic tools. The library is well documented and efficient, and allows researchers to quickly develop simple, yet powerful scripts that enable complex genomic analyses.$\backslash$n$\backslash$nAVAILABILITY: pybedtools is maintained under the GPL license. Stable versions of pybedtools as well as documentation are available on the Python Package Index at http://pypi.python.org/pypi/pybedtools.$\backslash$n$\backslash$nCONTACT: dalerr@niddk.nih.gov; arq5x@virginia.edu$\backslash$n$\backslash$nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
author = {Dale, Ryan K. and Pedersen, Brent S. and Quinlan, Aaron R.},
doi = {10.1093/bioinformatics/btr539},
isbn = {1367-4811 (Electronic)$\backslash$r1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {24},
pages = {3423--3424},
pmid = {21949271},
title = {{Pybedtools: A flexible Python library for manipulating genomic datasets and annotations}},
volume = {27},
year = {2011}
}
@article{Langmead2009,
abstract = {Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source (http://bowtie.cbcb.umd.edu).},
author = {Langmead, Ben and Trapnell, Cole and Pop, Mihai and Salzberg, Steven L},
doi = {10.1186/gb-2009-10-3-r25},
isbn = {1465-6914 (Electronic)$\backslash$r1465-6906 (Linking)},
issn = {1465-6906},
journal = {Genome Biol},
keywords = {Algorithms,Base Sequence,Genome: Human,Humans,Sequence Alignment,bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
pages = {R25},
pmid = {19261174},
title = {{Ultrafast and memory-efficient alignment of short DNA sequences to the human genome}},
volume = {10},
year = {2009}
}
@article{Dobin2013,
abstract = {MOTIVATION: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.$\backslash$n$\backslash$nRESULTS: To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90{\%} success rate, corroborating the high precision of the STAR mapping strategy.$\backslash$n$\backslash$nAVAILABILITY AND IMPLEMENTATION: STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.},
author = {Dobin, Alexander and Davis, Carrie A. and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R.},
doi = {10.1093/bioinformatics/bts635},
isbn = {1367-4811 (Electronic)$\backslash$n1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {1},
pages = {15--21},
pmid = {23104886},
title = {{STAR: Ultrafast universal RNA-seq aligner}},
volume = {29},
year = {2013}
}
@article{Tarasov2015,
abstract = {UNLABELLED: Sambamba is a high-performance robust tool and library for working with SAM, BAM and CRAM sequence alignment files; the most common file formats for aligned next generation sequencing data. Sambamba is a faster alternative to samtools that exploits multi-core processing and dramatically reduces processing time. Sambamba is being adopted at sequencing centers, not only because of its speed, but also because of additional functionality, including coverage analysis and powerful filtering capability. AVAILABILITY AND IMPLEMENTATION: Sambamba is free and open source software, available under a GPLv2 license. Sambamba can be downloaded and installed from http://www.open-bio.org/wiki/Sambamba.Sambamba v0.5.0 was released with doi:10.5281/zenodo.13200.},
author = {Tarasov, Artem and Vilella, Albert J. and Cuppen, Edwin and Nijman, Isaac J. and Prins, Pjotr},
doi = {10.1093/bioinformatics/btv098},
issn = {14602059},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {12},
pages = {2032--2034},
pmid = {25697820},
title = {{Sambamba: Fast processing of NGS alignment formats}},
volume = {31},
year = {2015}
}
@article{Didion2017,
abstract = {A key step in the transformation of raw sequencing reads into biological insights is the trimming of adapter sequences and low-quality bases. Read trimming has been shown to increase the quality and reliability while decreasing the computational requirements of downstream analyses. Many read trimming software tools are available; however, no tool simultaneously provides the accuracy, computational efficiency, and feature set required to handle the types and volumes of data generated in modern sequencing-based experiments. Here we introduce Atropos and show that it trims reads with high sensitivity and specificity while maintaining leading-edge speed. Compared to other state-of-the-art read trimming tools, Atropos achieves significant increases in trimming accuracy while remaining competitive in execution times. Furthermore, Atropos maintains high accuracy even when trimming data with elevated rates of sequencing errors. The accuracy, high performance, and broad feature set offered by Atropos makes it an appropriate choice for the pre-processing of Illumina, ABI SOLiD, and other current-generation short-read sequencing datasets. Availability. Atropos is open source and free software written in Python (3.3+) and available at https://github.com/jdidion/atropos.},
author = {Didion, John P and Martin, Marcel and Collins, Francis S},
doi = {10.7287/peerj.preprints.2452v4},
issn = {2167-9843},
keywords = {Adapter,Cutadapt,Illumina,NGS,Preprocessing,Read,Sequencing,Trimming},
mendeley-groups = {Bioinfo/rnaseq},
month = {jan},
publisher = {PeerJ Inc.},
title = {{Atropos: specific, sensitive, and speedy trimming of sequencing reads}},
url = {https://peerj.com/preprints/2452/},
year = {2017}
}
@article{Li2011,
abstract = {MOTIVATION: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty.$\backslash$n$\backslash$nRESULTS: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors.$\backslash$n$\backslash$nAVAILABILITY: http://samtools.sourceforge.net.$\backslash$n$\backslash$nCONTACT: hengli@broadinstitute.org.},
archivePrefix = {arXiv},
arxivId = {1203.6372},
author = {Li, Heng},
doi = {10.1093/bioinformatics/btr509},
eprint = {1203.6372},
isbn = {1367-4811 (Electronic)$\backslash$r1367-4803 (Linking)},
issn = {13674803},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {21},
pages = {2987--2993},
pmid = {21903627},
title = {{A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data}},
volume = {27},
year = {2011}
}
@article{Li2009,
abstract = {SUMMARY: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. AVAILABILITY: http://samtools.sourceforge.net.},
archivePrefix = {arXiv},
arxivId = {1006.1266v2},
author = {Li, Heng and Handsaker, Bob and Wysoker, Alec and Fennell, Tim and Ruan, Jue and Homer, Nils and Marth, Gabor and Abecasis, Goncalo and Durbin, Richard},
doi = {10.1093/bioinformatics/btp352},
eprint = {1006.1266v2},
isbn = {1367-4803$\backslash$r1460-2059},
issn = {13674803},
journal = {Bioinformatics},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {16},
pages = {2078--2079},
pmid = {19505943},
title = {{The Sequence Alignment/Map format and SAMtools}},
volume = {25},
year = {2009}
}
@article{Friedl??nder2012,
abstract = {microRNAs (miRNAs) are a large class of small non-coding RNAs which post-transcriptionally regulate the expression of a large fraction of all animal genes and are important in a wide range of biological processes. Recent advances in high-throughput sequencing allow miRNA detection at unprecedented sensitivity, but the computational task of accurately identifying the miRNAs in the background of sequenced RNAs remains challenging. For this purpose, we have designed miRDeep2, a substantially improved algorithm which identifies canonical and non-canonical miRNAs such as those derived from transposable elements and informs on high-confidence candidates that are detected in multiple independent samples. Analyzing data from seven animal species representing the major animal clades, miRDeep2 identified miRNAs with an accuracy of 98.6-99.9{\%} and reported hundreds of novel miRNAs. To test the accuracy of miRDeep2, we knocked down the miRNA biogenesis pathway in a human cell line and sequenced small RNAs before and after. The vast majority of the >100 novel miRNAs expressed in this cell line were indeed specifically downregulated, validating most miRDeep2 predictions. Last, a new miRNA expression profiling routine, low time and memory usage and user-friendly interactive graphic output can make miRDeep2 useful to a wide range of researchers.},
author = {Friedl??nder, Marc R. and MacKowiak, Sebastian D. and Li, Na and Chen, Wei and Rajewsky, Nikolaus},
doi = {10.1093/nar/gkr688},
isbn = {1362-4962 (Electronic)$\backslash$r0305-1048 (Linking)},
issn = {03051048},
journal = {Nucleic Acids Research},
keywords = {bcbio-srnaseq},
mendeley-groups = {Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {1},
pages = {37--52},
pmid = {21911355},
title = {{MiRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades}},
volume = {40},
year = {2012}
}
@article{Selitsky2015,
abstract = {BACKGROUND: Small RNA-sequencing has revealed the diversity and high abundance of small RNAs derived from tRNAs, referred to as tRNA-derived RNAs. However, at present, there is no standardized nomenclature and there are no methods for accurate annotation and quantification of these small RNAs. tRNA-derived RNAs have unique features that limit the utility of conventional alignment tools and quantification methods. RESULTS: We describe here the challenges of mapping, naming, and quantifying tRNA-derived RNAs and present a novel method that addresses them, called tDRmapper. We then use tDRmapper to perform a comparative analysis of tRNA-derived RNA profiles across different human cell types and diseases. We found that (1) tRNA-derived RNA profiles can differ dramatically across different cell types and disease states, (2) that positions and types of chemical modifications of tRNA-derived RNAs vary by cell type and disease, and (3) that entirely different tRNA-derived RNA species can be produced from the same parental tRNA depending on the cell type. CONCLUSION: tDRmappernot only provides a standardized nomenclature and quantification scheme, but also includes graphical visualization that facilitates the discovery of novel tRNA and tRNA-derived RNA biology.},
author = {Selitsky, Sara R and Sethupathy, Praveen},
doi = {10.1186/s12859-015-0800-0},
isbn = {10.1186/s12859-015-0800-0},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Bioinformatics,RNA modifications,Sequencing,bcbio-srnaseq,bioinformatics,rna modifications,sequencing,tDR,tRNA,tdr,trna},
mendeley-groups = {smallrna/others,Bioinfo/pipelines},
mendeley-tags = {bcbio-srnaseq},
number = {1},
pages = {354},
pmid = {26530785},
title = {{tDRmapper: challenges and solutions to mapping, naming, and quantifying tRNA-derived RNAs from human small RNA-sequencing data.}},
url = {http://www.biomedcentral.com/1471-2105/16/354},
volume = {16},
year = {2015}
}
@article{Pantano2015,
abstract = {MOTIVATION: Most computational tools for small non-coding RNAs (sRNA) sequencing data analysis focus in microRNAs (miRNAs), overlooking other types of sRNAs that show multi-mapping hits. Here, we have developed a pipeline to non-redundantly quantify all types of sRNAs, and extract patterns of expression in biologically defined groups. We have used our tool to characterize and profile sRNAs in post-mortem brain samples of control individuals and Parkinson's disease (PD) cases at early-premotor and late-symptomatic stages. RESULTS: Clusters of co-expressed sRNAs mapping onto tRNAs significantly separated premotor and motor cases from controls. A similar result was obtained using a matrix of miRNAs slightly varying in sequence (isomiRs). The present framework revealed sRNA alterations at premotor stages of PD, which might reflect initial pathogenic perturbations. This tool may be useful to discover sRNA expression patterns linked to different biological conditions. AVAILABILITY AND IMPLEMENTATION: The full code is available at http://github.com/lpantano/seqbuster. CONTACT: lpantano@hsph.harvard.edu or eulalia.marti@crg.euSupplementary information: Supplementary data are available at Bioinformatics online.},
author = {Pantano, Lorena and Friedlander, Marc R and Escaramis, Georgia and Lizano, Esther and Pallares-Albanell, Joan and Ferrer, Isidre and Estivill, Xavier and Marti, Eulalia},
doi = {10.1093/bioinformatics/btv632},
issn = {1367-4811 (Electronic)},
journal = {Bioinformatics (Oxford, England)},
keywords = {bcbio-srnaseq},
mendeley-tags = {bcbio-srnaseq},
month = {nov},
pmid = {26530722},
title = {{Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26530722},
year = {2015}
}
@article{Pantano2010,
abstract = {High-throughput sequencing technologies enable direct approaches to catalog and analyze snapshots of the total small RNA content of living cells. Characterization of high-throughput sequencing data requires bioinformatic tools offering a wide perspective of the small RNA transcriptome. Here we present SeqBuster, a highly versatile and reliable web-based toolkit to process and analyze large-scale small RNA datasets. The high flexibility of this tool is illustrated by the multiple choices offered in the pre-analysis for mapping purposes and in the different analysis modules for data manipulation. To overcome the storage capacity limitations of the web-based tool, SeqBuster offers a stand-alone version that permits the annotation against any custom database. SeqBuster integrates multiple analyses modules in a unique platform and constitutes the first bioinformatic tool offering a deep characterization of miRNA variants (isomiRs). The application of SeqBuster to small-RNA datasets of human embryonic stem cells revealed that most miRNAs present different types of isomiRs, some of them being associated to stem cell differentiation. The exhaustive description of the isomiRs provided by SeqBuster could help to identify miRNA-variants that are relevant in physiological and pathological processes. SeqBuster is available at http://estivilllab.crg.es/seqbuster.},
author = {Pantano, Lorena and Estivill, Xavier and Mart{\'{\i}}, Eul{\`{a}}lia},
institution = {Genetic Causes of Disease Group, Genes and Disease Program, Centre for Genomic Regulation, Pompeu Fabra University, Barcelona, Catalonia, Spain.},
journal = {Nucleic Acids Research},
keywords = {bcbio-srnaseq,cell differentiation,computational biology,embryonic stem cells,embryonic stem cells cytology,embryonic stem cells metabolism,genetic variation,humans,micrornas,micrornas chemistry,micrornas metabolism,rna,sequence analysis,software},
mendeley-tags = {bcbio-srnaseq},
number = {5},
pages = {e34},
publisher = {Oxford University Press},
title = {{SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/20008100},
volume = {38},
year = {2010}
}
