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      Splatter: simulation of single-cell RNA sequencing data

      1 , 2 , 1 , , 1 , 2

      Genome Biology

      BioMed Central

      Single-cell, RNA-seq, Simulation, Software

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          Abstract

          As single-cell RNA sequencing (scRNA-seq) technologies have rapidly developed, so have analysis methods. Many methods have been tested, developed, and validated using simulated datasets. Unfortunately, current simulations are often poorly documented, their similarity to real data is not demonstrated, or reproducible code is not available. Here, we present the Splatter Bioconductor package for simple, reproducible, and well-documented simulation of scRNA-seq data. Splatter provides an interface to multiple simulation methods including Splat, our own simulation, based on a gamma-Poisson distribution. Splat can simulate single populations of cells, populations with multiple cell types, or differentiation paths.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-017-1305-0) contains supplementary material, which is available to authorized users.

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          Most cited references 19

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

           ,  ,   (2013)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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            Counting absolute numbers of molecules using unique molecular identifiers.

            Counting individual RNA or DNA molecules is difficult because they are hard to copy quantitatively for detection. To overcome this limitation, we applied unique molecular identifiers (UMIs), which make each molecule in a population distinct, to genome-scale human karyotyping and mRNA sequencing in Drosophila melanogaster. Use of this method can improve accuracy of almost any next-generation sequencing method, including chromatin immunoprecipitation-sequencing, genome assembly, diagnostics and manufacturing-process control and monitoring.
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              A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures

               Hiroshi Akima (1970)
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                Author and article information

                Contributors
                luke.zappia@mcri.edu.au
                belinda.phipson@mcri.edu.au
                alicia.oshlack@mcri.edu.au
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                12 September 2017
                12 September 2017
                2017
                : 18
                Affiliations
                [1 ]Murdoch Childrens Research Institute, Royal Children’s Hospital, 50 Flemington Rd, Parkville, VIC 3052 Australia
                [2 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, School of Biosciences, The University of Melbourne, ; Parkville, VIC 3010 Australia
                Article
                1305
                10.1186/s13059-017-1305-0
                5596896
                28899397
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Method
                Custom metadata
                © The Author(s) 2017

                Genetics

                single-cell, rna-seq, simulation, software

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