@ARTICLE{Kim2013-vg,
  title       = {{TopHat2}: accurate alignment of transcriptomes in the
                 presence of insertions, deletions and gene fusions},
  author      = "Kim, Daehwan and Pertea, Geo and Trapnell, Cole and Pimentel,
                 Harold and Kelley, Ryan and Salzberg, Steven L",
  affiliation = "Center for Bioinformatics and Computational Biology,
                 University of Maryland, College Park, MD, 20742, USA.
                 infphilo@umiacs.umd.edu.",
  journal     = "Genome Biol.",
  volume      =  14,
  number      =  4,
  pages       = "R36",
  month       =  "25~" # apr,
  year        =  2013,
  url         = "http://dx.doi.org/10.1186/gb-2013-14-4-r36",
  issn        = "1465-6906",
  pmid        = "23618408",
  doi         = "10.1186/gb-2013-14-4-r36"
}
@ARTICLE{Langmead2012-bs,
  title     = {Fast gapped-read alignment with Bowtie 2},
  author    = "Langmead, Ben and Salzberg, Steven L",
  journal   = "Nat. Methods",
  publisher = "Nature Publishing Group",
  volume    =  9,
  number    =  4,
  pages     = "357--359",
  month     =  apr,
  year      =  2012,
  url       = "http://dx.doi.org/10.1038/nmeth.1923",
  issn      = "1548-7091",
  pmid      = "22388286",
  doi       = "10.1038/nmeth.1923"
}
@ARTICLE{Howard2013-fq,
  title       = {High-throughput {RNA} sequencing of pseudomonas-infected
                 Arabidopsis reveals hidden transcriptome complexity and novel
                 splice variants},
  author      = "Howard, Brian E and Hu, Qiwen and Babaoglu, Ahmet Can and
                 Chandra, Manan and Borghi, Monica and Tan, Xiaoping and He,
                 Luyan and Winter-Sederoff, Heike and Gassmann, Walter and
                 Veronese, Paola and Heber, Steffen",
  affiliation = "Department of Computer Science, North Carolina State
                 University, Raleigh, North Carolina, United States of America.",
  journal     = "PLoS One",
  volume      =  8,
  number      =  10,
  pages       = "e74183",
  month       =  "1~" # oct,
  year        =  2013,
  url         = "http://dx.doi.org/10.1371/journal.pone.0074183",
  issn        = "1932-6203",
  pmid        = "24098335",
  doi         = "10.1371/journal.pone.0074183",
  pmc         = "PMC3788074"
}
@ARTICLE{Li2009-oc,
  title   = {Fast and accurate short read alignment with {Burrows-Wheeler}
             transform},
  author  = "Li, H and Durbin, R",
  journal = "Bioinformatics",
  volume  =  25,
  number  =  14,
  pages   = "1754--1760",
  month   =  jul,
  year    =  2009,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btp324",
  issn    = "1367-4803",
  pmid    = "19451168",
  doi     = "10.1093/bioinformatics/btp324"
}
@ARTICLE{Li2013-oy,
  title         = "Aligning sequence reads, clone sequences and assembly
                   contigs with {BWA-MEM}",
  author        = "Li, Heng",
  abstract      = "Summary: BWA-MEM is a new alignment algorithm for aligning
                   sequence reads or long query sequences against a large
                   reference genome such as human. It automatically chooses
                   between local and end-to-end alignments, supports paired-end
                   reads and performs chimeric alignment. The algorithm is
                   robust to sequencing errors and applicable to a wide range
                   of sequence lengths from 70bp to a few megabases. For
                   mapping 100bp sequences, BWA-MEM shows better performance
                   than several state-of-art read aligners to date.
                   Availability and implementation: BWA-MEM is implemented as a
                   component of BWA, which is available at
                   http://github.com/lh3/bwa. Contact:
                   hengli@broadinstitute.org",
  journal       = "arXiv [q-bio.GN]",
  month         =  mar,
  year          =  2013,
  url           = "http://arxiv.org/abs/1303.3997",
  archivePrefix = "arXiv",
  primaryClass  = "q-bio.GN",
  eprint        = "1303.3997"
}
@ARTICLE{Liao2013-bn,
  title       = {The Subread aligner: fast, accurate and scalable read mapping
                 by seed-and-vote},
  author      = "Liao, Yang and Smyth, Gordon K and Shi, Wei",
  affiliation = "Division of Bioinformatics, The Walter and Eliza Hall
                 Institute of Medical Research, 1G Royal Parade, Parkville,
                 Victoria 3052, Australia.",
  journal     = "Nucleic Acids Res.",
  volume      =  41,
  number      =  10,
  pages       = "e108",
  month       =  "4~" # apr,
  year        =  2013,
  url         = "http://dx.doi.org/10.1093/nar/gkt214",
  issn        = "0305-1048",
  pmid        = "23558742",
  doi         = "10.1093/nar/gkt214",
  pmc         = "PMC3664803"
}
@MISC{Girke2014-oy,
  title       = {{systemPipeR}: {NGS} workflow and report generation
                 environment},
  author      = "Girke, Thomas",
  institution = "UC Riverside",
  month       =  "28~" # jun,
  year        =  2014,
  url         = "https://github.com/tgirke/systemPipeR"
}
@ARTICLE{Lawrence2013-kt,
  title       = {Software for computing and annotating genomic ranges},
  author      = "Lawrence, Michael and Huber, Wolfgang and Pag\`{e}s, Herv\'{e}
                 and Aboyoun, Patrick and Carlson, Marc and Gentleman, Robert
                 and Morgan, Martin T and Carey, Vincent J",
  affiliation = "Bioinformatics and Computational Biology, Genentech, Inc.,
                 South San Francisco, California, United States of America.
                 michafla@gene.com",
  journal     = "PLoS Comput. Biol.",
  volume      =  9,
  number      =  8,
  pages       = "e1003118",
  month       =  "8~" # aug,
  year        =  2013,
  url         = "http://dx.doi.org/10.1371/journal.pcbi.1003118",
  issn        = "1553-734X",
  pmid        = "23950696",
  doi         = "10.1371/journal.pcbi.1003118",
  pmc         = "PMC3738458"
}
@ARTICLE{Robinson2010-uk,
  title   = {edgeR: a Bioconductor package for differential expression analysis
             of digital gene expression data},
  author  = "Robinson, M D and McCarthy, D J and Smyth, G K",
  journal = "Bioinformatics",
  volume  =  26,
  number  =  1,
  pages   = "139--140",
  month   =  jan,
  year    =  2010,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btp616",
  issn    = "1367-4803",
  pmid    = "19910308",
  doi     = "10.1093/bioinformatics/btp616"
}
@ARTICLE{Love2014-sh,
  title   = {Moderated estimation of fold change and dispersion for {RNA-seq}
             data with {DESeq2}},
  author  = "Love, Michael and Huber, Wolfgang and Anders, Simon",
  journal = "Genome Biol.",
  volume  =  15,
  number  =  12,
  pages   = "550",
  year    =  2014,
  url     = "http://genomebiology.com/2014/15/12/550",
  issn    = "1465-6906",
  doi     = "10.1186/s13059-014-0550-8"
}
@ARTICLE{McKenna2010-ql,
  title       = {The Genome Analysis Toolkit: a {MapReduce} framework for
                 analyzing next-generation {DNA} sequencing data},
  author      = "McKenna, Aaron and Hanna, Matthew and Banks, Eric and
                 Sivachenko, Andrey and Cibulskis, Kristian and Kernytsky,
                 Andrew and Garimella, Kiran and Altshuler, David and Gabriel,
                 Stacey and Daly, Mark and DePristo, Mark A",
  affiliation = "Program in Medical and Population Genetics, The Broad
                 Institute of Harvard and MIT, Cambridge, Massachusetts 02142,
                 USA.",
  journal     = "Genome Res.",
  volume      =  20,
  number      =  9,
  pages       = "1297--1303",
  month       =  "19~" # jul,
  year        =  2010,
  url         = "http://dx.doi.org/10.1101/gr.107524.110",
  issn        = "1088-9051",
  pmid        = "20644199",
  doi         = "10.1101/gr.107524.110",
  pmc         = "PMC2928508"
}
@ARTICLE{Li2011-ll,
  title   = {A statistical framework for {SNP} calling, mutation discovery,
             association mapping and population genetical parameter estimation
             from sequencing data},
  author  = "Li, Heng",
  journal = "Bioinformatics",
  volume  =  27,
  number  =  21,
  pages   = "2987--2993",
  month   =  "1~" # nov,
  year    =  2011,
  url     = "http://bioinformatics.oxfordjournals.org/content/27/21/2987.abstract",
  issn    = "1367-4803",
  doi     = "10.1093/bioinformatics/btr509"
}
@ARTICLE{Wu2010-iq,
  title   = {Fast and {SNP-tolerant} detection of complex variants and splicing
             in short reads},
  author  = "Wu, T D and Nacu, S",
  journal = "Bioinformatics",
  volume  =  26,
  number  =  7,
  pages   = "873--881",
  month   =  apr,
  year    =  2010,
  url     = "http://dx.doi.org/10.1093/bioinformatics/btq057",
  issn    = "1367-4803",
  pmid    = "20147302",
  doi     = "10.1093/bioinformatics/btq057"
}
@ARTICLE{Zhang2008-pc,
  title   = {Model-based analysis of {ChIP-Seq} ({MACS})},
  author  = "Zhang, Y and Liu, T and Meyer, C A and Eeckhoute, J and Johnson, D
             S and Bernstein, B E and Nussbaum, C and Myers, R M and Brown, M
             and Li, W and Liu, X S",
  journal = "Genome Biol.",
  volume  =  9,
  number  =  9,
  year    =  2008,
  url     = "http://dx.doi.org/10.1186/gb-2008-9-9-r137",
  issn    = "1465-6906",
  pmid    = "18798982",
  doi     = "10.1186/gb-2008-9-9-r137"
}

@ARTICLE{Yu2015-xu,
  title       = {{ChIPseeker}: an {R/Bioconductor} package for {ChIP} peak
                 annotation, comparison and visualization},
  author      = "Yu, Guangchuang and Wang, Li-Gen and He, Qing-Yu",
  affiliation = "Key Laboratory of Functional Protein Research of Guangdong
                 Higher Education Institutes, College of Life Science and
                 Technology, Jinan University, Guangzhou 510632, China, State
                 Key Laboratory of Emerging Infectious Diseases, School of
                 Public Health, The University of Hong Kong, Hong Kong SAR,
                 China and. Guangdong Information Center, Guangzhou 510031,
                 China. Key Laboratory of Functional Protein Research of
                 Guangdong Higher Education Institutes, College of Life Science
                 and Technology, Jinan University, Guangzhou 510632, China.",
  journal     = "Bioinformatics",
  volume      =  31,
  number      =  14,
  pages       = "2382--2383",
  month       =  "15~" # jul,
  year        =  2015,
  url         = "http://dx.doi.org/10.1093/bioinformatics/btv145",
  issn        = "1367-4803, 1367-4811",
  pmid        = "25765347",
  doi         = "10.1093/bioinformatics/btv145"
}

@ARTICLE{Zhu2010-zo,
  title       = {{ChIPpeakAnno}: a Bioconductor package to annotate {ChIP-seq}
                 and {ChIP-chip} data},
  author      = "Zhu, Lihua J and Gazin, Claude and Lawson, Nathan D and
                 Pag\`{e}s, Herv\'{e} and Lin, Simon M and Lapointe, David S
                 and Green, Michael R",
  affiliation = "Program in Gene Function and Expression, University of
                 Massachusetts Medical School, Worcester, Massachusetts 01605,
                 USA. julie.zhu@umassmed.edu",
  journal     = "BMC Bioinformatics",
  volume      =  11,
  pages       = "237",
  month       =  "11~" # may,
  year        =  2010,
  url         = "http://dx.doi.org/10.1186/1471-2105-11-237",
  issn        = "1471-2105",
  pmid        = "20459804",
  doi         = "10.1186/1471-2105-11-237",
  pmc         = "PMC3098059"
}

@ARTICLE{Juntawong2014-ny,
  title       = {Translational dynamics revealed by genome-wide profiling of
                 ribosome footprints in Arabidopsis},
  author      = "Juntawong, Piyada and Girke, Thomas and Bazin, J\'{e}r\'{e}mie
                 and Bailey-Serres, Julia",
  affiliation = "Center for Plant Cell Biology and Department of Botany and
                 Plant Sciences, University of California, Riverside, CA 92521.",
  journal     = "Proc. Natl. Acad. Sci. U. S. A.",
  volume      =  111,
  number      =  1,
  pages       = "E203--12",
  month       =  "7~" # jan,
  year        =  2014,
  url         = "http://dx.doi.org/10.1073/pnas.1317811111",
  annote      = "PMID: 24367078",
  keywords    = "alternative splicing; long intergenic noncoding RNA; ribosome
                 profiling; translational efficiency; uORF",
  issn        = "0027-8424, 1091-6490",
  pmid        = "24367078",
  doi         = "10.1073/pnas.1317811111",
  pmc         = "PMC3890782"
}
@ARTICLE{Ingolia2009-cb,
  title   = {Genome-wide analysis in vivo of translation with nucleotide
             resolution using ribosome profiling},
  author  = "Ingolia, N T and Ghaemmaghami, S and Newman, J R and Weissman, J S",
  journal = "Science",
  volume  =  324,
  number  =  5924,
  pages   = "218--223",
  month   =  apr,
  year    =  2009,
  url     = "http://dx.doi.org/10.1016/j.ymeth.2009.03.016",
  issn    = "0036-8075",
  pmid    = "19213877",
  doi     = "10.1016/j.ymeth.2009.03.016"
}
@ARTICLE{Aspden2014-uu,
  title       = {Extensive translation of small Open Reading Frames revealed by
                 {Poly-Ribo-Seq}},
  author      = "Aspden, Julie L and Eyre-Walker, Ying Chen and Phillips, Rose
                 J and Amin, Unum and Mumtaz, Muhammad Ali S and Brocard,
                 Michele and Couso, Juan-Pablo",
  affiliation = "School of Life Sciences, University of Sussex, Brighton,
                 United Kingdom. School of Life Sciences, University of Sussex,
                 Brighton, United Kingdom. School of Life Sciences, University
                 of Sussex, Brighton, United Kingdom. School of Life Sciences,
                 University of Sussex, Brighton, United Kingdom. School of Life
                 Sciences, University of Sussex, Brighton, United Kingdom.
                 School of Life Sciences, University of Sussex, Brighton,
                 United Kingdom. School of Life Sciences, University of Sussex,
                 Brighton, United Kingdom.",
  journal     = "Elife",
  volume      =  3,
  pages       = "e03528",
  month       =  "21~" # aug,
  year        =  2014,
  url         = "http://dx.doi.org/10.7554/eLife.03528",
  keywords    = "biochemistry; d. melanogaster; evolutionary biology; genomics;
                 non-coding RNAs; small open reading Frames; transmembrane
                 peptides",
  issn        = "2050-084X",
  pmid        = "25144939",
  doi         = "10.7554/eLife.03528",
  pmc         = "PMC4359375"
}
@ARTICLE{Ingolia2011-fc,
  title   = {Ribosome profiling of mouse embryonic stem cells reveals the
             complexity and dynamics of mammalian proteomes},
  author  = "Ingolia, N T and Lareau, L F and Weissman, J S",
  journal = "Cell",
  volume  =  147,
  number  =  4,
  pages   = "789--802",
  month   =  "11~" # nov,
  year    =  2011,
  url     = "http://www.ncbi.nlm.nih.gov/pubmed/22056041",
  issn    = "0092-8674, 1097-4172;0092-8674",
  pmid    = "22056041",
  doi     = "10.1016/j.cell.2011.10.002"
}
@ARTICLE{Juntawong2015-ru,
  title       = {Ribosome profiling: a tool for quantitative evaluation of
                 dynamics in {mRNA} translation},
  author      = "Juntawong, Piyada and Hummel, Maureen and Bazin, Jeremie and
                 Bailey-Serres, Julia",
  affiliation = "Center for Plant Cell Biology and Department of Botany and
                 Plant Sciences, University of California, Riverside, CA,
                 92521, USA.",
  journal     = "Methods Mol. Biol.",
  volume      =  1284,
  pages       = "139--173",
  year        =  2015,
  url         = "http://dx.doi.org/10.1007/978-1-4939-2444-8_7",
  issn        = "1064-3745, 1940-6029",
  pmid        = "25757771",
  doi         = "10.1007/978-1-4939-2444-8\_7"
}
@ARTICLE{H_Backman2016-bt,
  title       = "{systemPipeR: NGS workflow and report generation environment}",
  author      = "H Backman, Tyler W and Girke, Thomas",
  affiliation = "Institute for Integrative Genome Biology, University of
                 California, Riverside, 1207F Genomics Building, 3401 Watkins
                 Drive, Riverside, 92521, CA, USA. Institute for Integrative
                 Genome Biology, University of California, Riverside, 1207F
                 Genomics Building, 3401 Watkins Drive, Riverside, 92521, CA,
                 USA. thomas.girke@ucr.edu.",
  journal     = "BMC Bioinformatics",
  volume      =  17,
  number      =  1,
  pages       = "388",
  month       =  "20~" # sep,
  year        =  2016,
  url         = "http://dx.doi.org/10.1186/s12859-016-1241-0",
  keywords    = "Analysis workflow; ChIP-Seq; Next Generation Sequencing (NGS);
                 RNA-Seq; Ribo-Seq; VAR-Seq",
  language    = "en",
  issn        = "1471-2105",
  pmid        = "27650223",
  doi         = "10.1186/s12859-016-1241-0",
  pmc         = "PMC5029110"
}
@ARTICLE{Kim2015-ve,
  title    = "{HISAT}: a fast spliced aligner with low memory requirements",
  author   = "Kim, Daehwan and Langmead, Ben and Salzberg, Steven L",
  abstract = "HISAT (hierarchical indexing for spliced alignment of
              transcripts) is a highly efficient system for aligning reads from
              RNA sequencing experiments. HISAT uses an indexing scheme based
              on the Burrows-Wheeler transform and the Ferragina-Manzini (FM)
              index, employing two types of indexes for alignment: a
              whole-genome FM index to anchor each alignment and numerous local
              FM indexes for very rapid extensions of these alignments. HISAT's
              hierarchical index for the human genome contains 48,000 local FM
              indexes, each representing a genomic region of ∼64,000 bp. Tests
              on real and simulated data sets showed that HISAT is the fastest
              system currently available, with equal or better accuracy than
              any other method. Despite its large number of indexes, HISAT
              requires only 4.3 gigabytes of memory. HISAT supports genomes of
              any size, including those larger than 4 billion bases.",
  journal  = "Nat. Methods",
  volume   =  12,
  number   =  4,
  pages    = "357--360",
  month    =  apr,
  year     =  2015,
  language = "en"
}
@Manual{Rsamtools,
    title = {Rsamtools: Binary alignment (BAM), FASTA, variant call (BCF), and tabix
file import},
    author = {Martin Morgan and Herv\'e Pag\`es and Valerie Obenchain and Nathaniel Hayden},
    year = {2019},
    note = {R package version 2.0.0},
    url = {http://bioconductor.org/packages/Rsamtools},
}
@ARTICLE{Anders2010-tp,
  title    = "Differential expression analysis for sequence count data",
  author   = "Anders, Simon and Huber, Wolfgang",
  abstract = "High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or
              barcode counting provide quantitative readouts in the form of
              count data. To infer differential signal in such data correctly
              and with good statistical power, estimation of data variability
              throughout the dynamic range and a suitable error model are
              required. We propose a method based on the negative binomial
              distribution, with variance and mean linked by local regression
              and present an implementation, DESeq, as an R/Bioconductor
              package.",
  journal  = "Genome Biol.",
  volume   =  11,
  number   =  10,
  pages    = "R106",
  month    =  oct,
  year     =  2010,
  language = "en"
}
@ARTICLE{Martin2011-bh,
  title    = "Cutadapt removes adapter sequences from high-throughput
              sequencing reads",
  author   = "Martin, Marcel",
  abstract = "When small RNA is sequenced on current sequencing machines, the
              resulting reads are usually longer than the RNA and therefore
              contain parts of the 3' adapter. That adapter must be found and
              removed error-tolerantly from each read before read mapping.
              Previous solutions are either hard to use or do not offer
              required features, in particular support for color space data. As
              an easy to use alternative, we developed the command-line tool
              cutadapt, which supports 454, Illumina and SOLiD (color space)
              data, offers two adapter trimming algorithms, and has other
              useful features. Cutadapt, including its MIT-licensed source
              code, is available for download at
              http://code.google.com/p/cutadapt/",
  journal  = "EMBnet.journal",
  volume   =  17,
  number   =  1,
  pages    = "10--12",
  month    =  may,
  year     =  2011,
  keywords = "next generation sequencing; small RNA; microRNA; adapter removal",
  language = "en"
}

@ARTICLE{Bolger2014-yr,
  title    = "Trimmomatic: a flexible trimmer for Illumina sequence data",
  author   = "Bolger, Anthony M and Lohse, Marc and Usadel, Bjoern",
  abstract = "MOTIVATION: Although many next-generation sequencing (NGS) read
              preprocessing tools already existed, we could not find any tool
              or combination of tools that met our requirements in terms of
              flexibility, correct handling of paired-end data and high
              performance. We have developed Trimmomatic as a more flexible and
              efficient preprocessing tool, which could correctly handle
              paired-end data. RESULTS: The value of NGS read preprocessing is
              demonstrated for both reference-based and reference-free tasks.
              Trimmomatic is shown to produce output that is at least
              competitive with, and in many cases superior to, that produced by
              other tools, in all scenarios tested. AVAILABILITY AND
              IMPLEMENTATION: Trimmomatic is licensed under GPL V3. It is
              cross-platform (Java 1.5+ required) and available at
              http://www.usadellab.org/cms/index.php?page=trimmomatic CONTACT:
              usadel@bio1.rwth-aachen.de SUPPLEMENTARY INFORMATION:
              Supplementary data are available at Bioinformatics online.",
  journal  = "Bioinformatics",
  volume   =  30,
  number   =  15,
  pages    = "2114--2120",
  month    =  aug,
  year     =  2014,
  language = "en"
}

% The entry below contains non-ASCII chars that could not be converted
% to a LaTeX equivalent.
@ARTICLE{Ziemann2016-mp,
  title    = "Evaluation of {microRNA} alignment techniques",
  author   = "Ziemann, Mark and Kaspi, Antony and El-Osta, Assam",
  abstract = "Genomic alignment of small RNA (smRNA) sequences such as
              microRNAs poses considerable challenges due to their short length
              (∼21 nucleotides [nt]) as well as the large size and complexity
              of plant and animal genomes. While several tools have been
              developed for high-throughput mapping of longer mRNA-seq reads
              (>30 nt), there are few that are specifically designed for
              mapping of smRNA reads including microRNAs. The accuracy of these
              mappers has not been systematically determined in the case of
              smRNA-seq. In addition, it is unknown whether these aligners
              accurately map smRNA reads containing sequence errors and
              polymorphisms. By using simulated read sets, we determine the
              alignment sensitivity and accuracy of 16 short-read mappers and
              quantify their robustness to mismatches, indels, and nontemplated
              nucleotide additions. These were explored in the context of a
              plant genome (Oryza sativa, ∼500 Mbp) and a mammalian genome
              (Homo sapiens, ∼3.1 Gbp). Analysis of simulated and real
              smRNA-seq data demonstrates that mapper selection impacts
              differential expression results and interpretation. These results
              will inform on best practice for smRNA mapping and enable more
              accurate smRNA detection and quantification of expression and RNA
              editing.",
  journal  = "RNA",
  volume   =  22,
  number   =  8,
  pages    = "1120--1138",
  month    =  aug,
  year     =  2016,
  keywords = "gene expression; microRNA; next-generation sequencing; short-read
              aligners; small RNA sequencing",
  language = "en"
}

@ARTICLE{Li2009-ys,
  title    = "Fast and accurate short read alignment with {Burrows-Wheeler}
              transform",
  author   = "Li, Heng and Durbin, Richard",
  abstract = "MOTIVATION: The enormous amount of short reads generated by the
              new DNA sequencing technologies call for the development of fast
              and accurate read alignment programs. A first generation of hash
              table-based methods has been developed, including MAQ, which is
              accurate, feature rich and fast enough to align short reads from
              a single individual. However, MAQ does not support gapped
              alignment for single-end reads, which makes it unsuitable for
              alignment of longer reads where indels may occur frequently. The
              speed of MAQ is also a concern when the alignment is scaled up to
              the resequencing of hundreds of individuals. RESULTS: We
              implemented Burrows-Wheeler Alignment tool (BWA), a new read
              alignment package that is based on backward search with
              Burrows-Wheeler Transform (BWT), to efficiently align short
              sequencing reads against a large reference sequence such as the
              human genome, allowing mismatches and gaps. BWA supports both
              base space reads, e.g. from Illumina sequencing machines, and
              color space reads from AB SOLiD machines. Evaluations on both
              simulated and real data suggest that BWA is approximately 10-20x
              faster than MAQ, while achieving similar accuracy. In addition,
              BWA outputs alignment in the new standard SAM (Sequence
              Alignment/Map) format. Variant calling and other downstream
              analyses after the alignment can be achieved with the open source
              SAMtools software package. AVAILABILITY:
              http://maq.sourceforge.net.",
  journal  = "Bioinformatics",
  volume   =  25,
  number   =  14,
  pages    = "1754--1760",
  month    =  jul,
  year     =  2009,
  language = "en"
}

@ARTICLE{Tam2015-gb,
  title    = "Optimization of {miRNA-seq} data preprocessing",
  author   = "Tam, Shirley and Tsao, Ming-Sound and McPherson, John D",
  abstract = "The past two decades of microRNA (miRNA) research has solidified
              the role of these small non-coding RNAs as key regulators of many
              biological processes and promising biomarkers for disease. The
              concurrent development in high-throughput profiling technology
              has further advanced our understanding of the impact of their
              dysregulation on a global scale. Currently, next-generation
              sequencing is the platform of choice for the discovery and
              quantification of miRNAs. Despite this, there is no clear
              consensus on how the data should be preprocessed before
              conducting downstream analyses. Often overlooked, data
              preprocessing is an essential step in data analysis: the presence
              of unreliable features and noise can affect the conclusions drawn
              from downstream analyses. Using a spike-in dilution study, we
              evaluated the effects of several general-purpose aligners (BWA,
              Bowtie, Bowtie 2 and Novoalign), and normalization methods
              (counts-per-million, total count scaling, upper quartile scaling,
              Trimmed Mean of M, DESeq, linear regression, cyclic loess and
              quantile) with respect to the final miRNA count data
              distribution, variance, bias and accuracy of differential
              expression analysis. We make practical recommendations on the
              optimal preprocessing methods for the extraction and
              interpretation of miRNA count data from small RNA-sequencing
              experiments.",
  journal  = "Brief. Bioinform.",
  volume   =  16,
  number   =  6,
  pages    = "950--963",
  month    =  nov,
  year     =  2015,
  keywords = "data preprocessing; miRNA sequencing; miRNA-seq normalization;
              small RNA sequence alignment",
  language = "en"
}





















