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1 .. image:: https://raw.githubusercontent.com/mhammell-laboratory/bamqc/master/doc/bamqc-icon.png 1 =======
2 :width: 200 px 2 ezBAMQC
3 :alt: generated at codeology.braintreepayments.com/mhammell-laboratory/bamqc 3 =======
4 :align: right
5 :target: http://codeology.braintreepayments.com/mhammell-laboratory/bamqc
6 4
7 =====
8 ezBAMQC
9 =====
10 *"ezBAMQC, a tool to check the quality of mapped next generation sequencing files."* 5 *"ezBAMQC, a tool to check the quality of mapped next generation sequencing files."*
6
7 :Codeology Icon:
8
9 .. image:: https://raw.githubusercontent.com/mhammell-laboratory/bamqc/master/doc/bamqc-icon.gif
10 :alt: generated at codeology.braintreepayments.com/mhammell-laboratory/bamqc
11 :align: right
12 :target: http://codeology.braintreepayments.com/mhammell-laboratory/bamqc
11 13
12 :Description: 14 :Description:
13 15
14 ezBAMQC is a tool to check the quality of either one or many mapped next-generation-sequencing datasets. It conducts comprehensive evaluations of aligned sequencing data from multiple aspects including: clipping profile, mapping quality distribution, mapped read length distribution, genomic/transcriptomic mapping distribution, inner distance distribution (for paired-end reads), ribosomal RNA contamination, transcript 5’ and 3’ end bias, transcription dropout rate, sample correlations, sample reproducibility, sample variations. It outputs a set of tables and plots and one HTML page that contains a summary of the results. Many metrics are designed for RNA-seq data specifically, but ezBAMQC can be applied to any mapped sequencing dataset such as RNA-seq, CLIP-seq, GRO-seq, ChIP-seq, DNA-seq and so on. :: 16 ezBAMQC is a tool to check the quality of either one or many mapped next-generation-sequencing datasets. It conducts comprehensive evaluations of aligned sequencing data from multiple aspects including: clipping profile, mapping quality distribution, mapped read length distribution, genomic/transcriptomic mapping distribution, inner distance distribution (for paired-end reads), ribosomal RNA contamination, transcript 5’ and 3’ end bias, transcription dropout rate, sample correlations, sample reproducibility, sample variations. It outputs a set of tables and plots and one HTML page that contains a summary of the results. Many metrics are designed for RNA-seq data specifically, but ezBAMQC can be applied to any mapped sequencing dataset such as RNA-seq, CLIP-seq, GRO-seq, ChIP-seq, DNA-seq and so on.
15 17
16 :Links: 18 :Links:
17 19
18 `Github Page <https://github.com/mhammell-laboratory/bamqc>`_ 20 `Github Page <https://github.com/mhammell-laboratory/bamqc>`_
19 21
22 `MHammell Lab <http://hammelllab.labsites.cshl.edu/software>`_ 24 `MHammell Lab <http://hammelllab.labsites.cshl.edu/software>`_
23 25
24 :Authors: 26 :Authors:
25 Ying Jin, David Molik, and Molly Hammell 27 Ying Jin, David Molik, and Molly Hammell
26 28
27 :Version: 0.6.5 29 :Version: 0.6.7
28 30
29 :Contact: 31 :Contact:
30 Ying Jin (yjin@cshl.edu) 32 Ying Jin (yjin@cshl.edu)
31 33
32 Installation guide for ezBAMQC for from source installs 34 Installation guide for ezBAMQC for from source installs
33 ===================================================== 35 =======================================================
34 36
35 When installing ezBAMQC there are several options, but the main point is: since ezBAMQC uses C++ STD 11 you'll need a version of GCC that can support that, this useally means 4.8 or 4.9. beyond that, you'll need Python, R and Corrplot for interfacing with the C code. 37 When installing ezBAMQC there are several options, but the main point is: since ezBAMQC uses C++ STD 11 you'll need a version of GCC that can support that, this useally means 4.8 or 4.9. beyond that, you'll need Python, R and Corrplot for interfacing with the C code.
36 38
37 :Intallation: 39 :Intallation:
38 `Source Code <https://github.com/mhammell-laboratory/ezBAMQC/releases>`_ 40 `Source Code <https://github.com/mhammell-laboratory/ezBAMQC/releases>`_
76 78
77 2) Unpack tarball and go to the directory of the package: 79 2) Unpack tarball and go to the directory of the package:
78 80
79 :: 81 ::
80 82
81 tar xvfz bamqc-0.6.6.tar.gz 83 tar xvfz bamqc-0.6.7.tar.gz
82 84
83 cd bamqc-0.6.6 85 cd bamqc-0.6.7
84 86
85 3) Run make: 87 3) Run make:
86 88
87 :: 89 ::
88 90
137 ezBAMQC -i test-data/exp_data/treat1.bam test-data/exp_data/treat2.bam test-data/exp_data/treat3.bam -r test-data/exp_data/hg9_refGene.gtf -q 30 --rRNA test-data/exp_data/hg19_rRNA.bed -o exp_output2 139 ezBAMQC -i test-data/exp_data/treat1.bam test-data/exp_data/treat2.bam test-data/exp_data/treat3.bam -r test-data/exp_data/hg9_refGene.gtf -q 30 --rRNA test-data/exp_data/hg19_rRNA.bed -o exp_output2
138 140
139 Please find the example output from folder test-data. 141 Please find the example output from folder test-data.
140 142
141 FAQ 143 FAQ
142 ==== 144 ===
143 Q: Why use ezBAMQC? 145 Q: Why use ezBAMQC?
144 146
145 A: ezBAMQC is efficient and easy to use. With one command line, it reports a comprehensive evaluation of the data with a set of plots and tables.The ability to assess multiple samples together with high efficiency make it especially useful in cases where there are a large number of samples from the same condition, genotype, or treatment. ezBAMQC was written in C++ and supports multithreading. A mouse RNA-seq sample with 120M alignments can be done in 8 minutes with 5 threads. 147 A: ezBAMQC is efficient and easy to use. With one command line, it reports a comprehensive evaluation of the data with a set of plots and tables.The ability to assess multiple samples together with high efficiency make it especially useful in cases where there are a large number of samples from the same condition, genotype, or treatment. ezBAMQC was written in C++ and supports multithreading. A mouse RNA-seq sample with 120M alignments can be done in 8 minutes with 5 threads.
146 148
147 Q: Why the total number of reads reported by ezBAMQC does not match with samtools flagstat? 149 Q: Why the total number of reads reported by ezBAMQC does not match with samtools flagstat?