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自学lncRNA-seq数据分析第一讲~学习大纲

ulwvfje — Fri, 08 Jul 2016 12:37:33 +0000

lncRNA分析跟常见的mRNA-seq分析重合度很高，无非也是把测序的fastq文件mapping到参加基因组，获取转录本信息，转录本表达定量，表达量的差异分析，比较新的分析就是把转录本分成了lncRNA和mRNA，这样可以考虑它们之间的互相作用，也可以在实验设计的时候加入miRNA和CHIP-seq，这样多种数据结合分析，显得更高大上一点，也能更好的刻画机体状态，从而回答生物学假设，我这里先列出我的自学大纲，如果有朋友想跟着我学习，可以发email给我，我的邮箱是jmzeng1314，是163邮箱，发信给我前，先看如果你希望我回答你的问题这篇文章：

step1:read paper and get the workflow for lncRNA anlaysis

## http://www.sciencedirect.com/science/article/pii/S1934590913000982

## Integration of Genome-wide Approaches Identifies lncRNAs of Adult Neural Stem Cells and Their Progeny In Vivo

step2:download the raw data from NCBI-GEO-SRA database

## http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45282

step3:quality control for the sequence data

## paper:2012- analysis of RNA-seq experiments with TopHat and Cufflinks : http://www.nature.com/nprot/journal/v7/n3/full/nprot.2012.016.html

## 作者用的是tophat2+cufflinks+CummeRbund 我这里替换成HISAT+StringTie+ballgown

step4:mapping the reads to reference genome/transcriptome

step5: de nove identification lncRNA

paper:Genome-wide computational identification and manual annotation of human long noncoding RNA genes :http://rnajournal.cshlp.org/content/16/8/1478.short

paper:A complete annotation of CaptureSeq-derived transcripts is available at http://neurosurgery.ucsf.edu/danlimlab/lncRNA.

step6:counts the expression lever for each LncRNA

step7:find the differentially expressed LncRNA

step8: function anlaysis for the lncRNA

paper:2016-Discovery and functional analysis of lncRNAs: http://www.sciencedirect.com/science/article/pii/S1874939915002163

paper:2014-Genome-wide screening and functional analysis identify a large number of long noncoding RNAs involved in the sexual reproduction of rice: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0512-1

Introduction: sequence-->expression-->function: http://www.exiqon.com/lncrna

paper:2014-lncRNAtor-a comprehensive resource for functional investigation of long noncoding RNAs:http://lncrnator.ewha.ac.kr/index.htm

paper:2014-Genome-Wide Analysis of Long Noncoding RNA (lncRNA) Expression in Hepatoblastoma Tissues : http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0085599

paper:2015- Predicting the Functions of Long Noncoding RNAs Using RNA-Seq Based on Bayesian Network: http://www.hindawi.com/journals/bmri/2015/839590/

paper:2016-Long Non-coding RNA in Neurons: New Players in Early Response to BDNF Stimulation : http://journal.frontiersin.org/article/10.3389/fnmol.2016.00015/full

Figure 7: GO analysis of the biological function of lncRNA: http://www.nature.com/articles/srep21499/figures/7

Figure 2. GO enrichment analysis of lncRNA targets. GO annotations of lncRNA targets categorized by (A) biological process, (B) cell component and (C) molecular function. :https://www.spandidos-publications.com/or/31/4/1613 ## http://www.oatext.com/Genome-wide-analysis-of-differentially-expressed-long-noncoding-RNAs-induced-by-low-shear-stress-in-human-umbilical-vein-endothelial-cells.php

Figure 3. Functional classification of lncRNA : https://www.spandidos-publications.com/ijo/45/2/619

paper:2016-Expression profiles of long-noncoding RNAs in cutaneous squamous cell carcinoma. : http://www.futuremedicine.com/doi/abs/10.2217/epi-2015-0012

step9:lncRNA-mRNA co-expression network

paper:2015-biomarkers-OV-Comprehensive analysis of lncRNA-mRNA co-expression patterns: http://www.nature.com/articles/srep17683

paper:2016-Differential lncRNA-mRNA co-expression network analysis : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732855/

paper:2014-Microarray Profiling and Co-Expression Network Analysis of Circulating lncRNAs and mRNAs :http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0093388

paper:2014-Long Noncoding RNA-EBIC Promotes Tumor Cell Invasion by Binding to EZH2 and Repressing E-Cadherin in Cervical Cancer : https://figshare.com/articles/_lncRNA_mRNA_co_expression_network_/1098837

paper:2016-Microarray Analysis of lncRNA and mRNA Expression Profiles in Patients with Neuromyelitis Optica : http://link.springer.com/article/10.1007/s12035-016-9754-0

paper:2015-LncRNA expression profiles reveal the co-expression network in human colorectal carcinoma : http://www.ijcep.com/files/ijcep0017983.pdf

step10: analysis of lncRNA-miRNA interactions

paper:2014-starBase-decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data : http://nar.oxfordjournals.org/content/early/2013/11/30/nar.gkt1248.short

paper:2014-An Integrated Analysis of miRNA, lncRNA, and mRNA Expression Profiles : http://www.hindawi.com/journals/bmri/2014/345605/abs/

paper:2013-Systematic transcriptome wide analysis of lncRNA-miRNA interactions : http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0053823

Figure: Regulatory cancer network of lncRNA-miRNA interactions. : http://www.aimspress.com/article/10.3934/molsci.2016.2.104/fulltext.html

paper:2014-Functional interactions among microRNAs and long noncoding RNAs :http://www.sciencedirect.com/science/article/pii/S1084952114001700

paper:2014-NPInter v2.0-an updated database of ncRNA interactions: http://nar.oxfordjournals.org/content/42/D1/D104.short

paper:2013-Long Noncoding RNAs-Related Diseases, Cancers, and Drugs: http://www.hindawi.com/journals/tswj/2013/943539/abs/

step11: Histone Modifications and lncRNA Expression

paper:2013-Panning for Long Noncoding RNAs : http://www.mdpi.com/2218-273X/3/1/226/htm

2015-Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin : https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0570-4

2013-Predicting long non-coding RNAs using RNA sequencing : http://www.ncbi.nlm.nih.gov/pubmed/23541739

2014-Identification of prostate cancer LncRNAs by RNA-Seq ： http://www.ncbi.nlm.nih.gov/pubmed/25422238

book: Identification of Disease-Related Genes by NGS: http://www.diss.fu-berlin.de/diss/servlets/MCRFileNodeServlet/FUDISS_derivate_000000015470/Dorn_Cornelia.diss2.pdf

book: yeast function genomic : http://download.springer.com/static/pdf/150/bok%253A978-1-4939-3079-1.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4939-3079-1&token2=exp=1467776101~acl=%2Fstatic%2Fpdf%2F150%2Fbok%25253A978-1-4939-3079-1.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4939-3079-1*~hmac=4aaa3f402c498fffa9c609d6ab14c14c0eba3b7862a526ecfddf30c0cf7fb81f

RNA-seq workflow tophat+cufflinks+R: https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/1100005/20130522GACDRNASeqandMethylation.pdf

mRNA and ncRNA (miRNA, lncRNA, snoRNA, etc)

Quantitative gene profiling of long noncoding RNAs with targeted RNA sequencing : http://www.nature.com/nmeth/journal/v12/n4/full/nmeth.3321.html

Targeted RNA sequencing reveals the deep complexity of the human transcriptome http://cole-trapnell-lab.github.io/pdfs/papers/mercer-capture-seq.pdf

Targeted sequencing for gene discovery and quantification using RNA CaptureSeq http://www.ncbi.nlm.nih.gov/pubmed/24705597

2011年 RNA CaptureSeq技术出现： http://www.ebiotrade.com/newsf/2011-11/20111117145845614.htm

RNA-seq比对软件HISAT说明书

ulwvfje — Sun, 10 May 2015 14:47:36 +0000

取代bowtie+tophat进行RNA-seq比对

HISAT全称为Hierarchical Indexing for Spliced Alignment of Transcripts，由约翰霍普金斯大学开发。它取代Bowtie/TopHat程序，能够将RNA-Seq的读取与基因组进行快速比对。这项成果发表在3月9日的《Nature Methods》上。

HISAT利用大量FM索引，以覆盖整个基因组。以人类基因组为例，它需要48,000个索引，每个索引代表~64,000 bp的基因组区域。这些小的索引结合几种比对策略，实现了RNA-Seq读取的高效比对，特别是那些跨越多个外显子的读取。尽管它利用大量索引，但HISAT只需要4.3 GB的内存。这种应用程序支持任何规模的基因组，包括那些超过40亿个碱基的。

HISAT软件可从以下地址获取：http://ccb.jhu.edu/software/hisat/index.shtml。

首先，我们安装这个软件！

Wget http://ccb.jhu.edu/software/hisat/downloads/hisat-0.1.5-beta-source.zip

官网下载的是源码包，需要make一下，make之后目录下面就多了很多程序，绿色的那些都是，看起来是不是很眼熟呀！！！

哈哈，这完全就是bowtie的模拟版本！！！

也可以从github里面下载，wget https://codeload.github.com/infphilo/hisat/zip/master

下载后直接解压即可使用啦。当然这个软件本身也有着详尽的说明书

http://ccb.jhu.edu/software/hisat/manual.shtml

然后就是准备数据，它跟tophat一样的功能。就是把用RNA-seq方法测序得到的fastq文件比对到参考基因组上面，所以就准这两个文件了哦

接下来是运行程序！

说明书上面写着分成两个步骤，构建索引和比对。

这个软件包模仿bowtie自带了一个example数据，而且它的说明书也是针对于那个example来的，我也简单运行一下。

$HISAT_HOME/hisat-build $HISAT_HOME/example/reference/22_20-21M.fa 22_20-21M_hisat

构建索引的命令如上，跟bowtie一样我修改了一下

/home/jmzeng/hoston/RNA-soft/hisat-0.1.5-beta/hisat-build 22_20-21M.fa my_hisat_index

连日志都跟bowtie一模一样，哈哈，可以看到我们的这个参考fasta文件 22_20-21M.fa 就变成索引文件啦，索引还是很多的！

然后就是比对咯，还是跟bowtie一样

$HISAT_HOME/hisat -x 22_20-21M_hisat -U $HISAT_HOME/example/reads/reads_1.fq -S eg1.sam

我的命令是

/home/jmzeng/hoston/RNA-soft/hisat-0.1.5-beta/hisat -x my_hisat_index -U ../reads/reads_1.fq -S reads1.sam

1000 reads; of these:

1000 (100.00%) were unpaired; of these:

0 (0.00%) aligned 0 times

1000 (100.00%) aligned exactly 1 time

0 (0.00%) aligned >1 times

100.00% overall alignment rate

哈哈，到这里。这个软件就运行完毕啦！！！是不是非常简单，只有你会用bowtie，这个就没有问题。当然啦，软件还是有很多细节是需要调整的。我下面就简单讲一个实际的例子哈！

首先，我用了1.5小时把4.6G的小鼠基因组构建了索引

/home/jmzeng/hoston/RNA-soft/hisat-0.1.5-beta/hisat-build Mus_musculus.GRCm38.fa.fa mouse_hisat_index

然后对我的四个测序文件进行比对。

for i in *fq

/home/jmzeng/hoston/RNA-soft/hisat-0.1.5-beta/hisat -x /home/jmzeng/hoston/mouse/mouse_hisat_index \

-p 30 -U $i.trimmed.single -S ./hisat_out/${i%.*}.sam

done

它运行的速度的确要比tophat快好多，太可怕的速度！！！！至于是否多消耗了内存我就没有看了

4.6G的小鼠，5G的测序数据，我只用了五个核，居然十分钟就跑完了！

然后听群友说是因为没有加 --known-splicesite-infile 这个参数的原因，没有用gtf文件来指导我们的RNA数据的比对，这样是不对的！

需要用下面这个脚本把gtf文件处理一下，然后导入什么那个参数来指导RNA比对。

extract_splice_sites.py genes.gtf > splicesites.txt

但是我报错了，错误很奇怪，没解决，但是我换了个 extract_splice_sites.py 程序，就可以运行啦！之前是HISAT 0.1.5-beta release 2/25/2015里面的python程序，后来我换做了github里面的就可以啦！

/home/jmzeng/hoston/RNA-soft/hisat-master/extract_splice_sites.py Mus_musculus.GRCm38.79.gtf >mouse_splicesites.txt

21192819 reads; of these:
21192819 (100.00%) were unpaired; of these:
14236834 (67.18%) aligned 0 times
5437800 (25.66%) aligned exactly 1 time
1518185 (7.16%) aligned >1 times

感觉没有变化，不知道为什么？

21192819 reads; of these:

21192819 (100.00%) were unpaired; of these:

14236838 (67.18%) aligned 0 times

5437793 (25.66%) aligned exactly 1 time

1518188 (7.16%) aligned >1 times

32.82% overall alignment rate

发表这个软件的文献本身也把这个软件跟其它软件做了详尽的对比

http://www.nature.com/nmeth/journal/v12/n4/full/nmeth.3317.html

Program	Run time (min)	Memory usage (GB)
Run times and memory usage for HISAT and other spliced aligners to align 109 million 101-bp RNA-seq reads from a lung fibroblast data set. We used three CPU cores to run the programs on a Mac Pro with a 3.7 GHz Quad-Core Intel Xeon E5 processor and 64 GB of RAM.
HISATx1	22.7	4.3
HISATx2	47.7	4.3
HISAT	26.7	4.3
STAR	25	28
STARx2	50.5	28
GSNAP	291.9	20.2
OLego	989.5	3.7
TopHat2	1,170	4.3

参考：http://www.plob.org/2015/03/20/8980.html

http://nextgenseek.com/2015/03/hisat-a-fast-and-memory-lean-rna-seq-aligner/