Pythonバイオ? Pythonバイオ/ツール?
4330 2019-06-09 (日) 06:56:26
Bowtie2最新版をインストール（アップデート）
ダウンロードしてunzipしてmake
使い方の例 biopapyrus bowtie2、マニュアル http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml
bowtie2-build --threads 16 GCF_000001405.38_GRCh38.p12_genomic.fna HG38.ix
Settings: Output files: "HG38.ix.*.bt2" Line rate: 6 (line is 64 bytes) Lines per side: 1 (side is 64 bytes) Offset rate: 4 (one in 16) FTable chars: 10 Strings: unpacked Max bucket size: default Max bucket size, sqrt multiplier: default Max bucket size, len divisor: 4 Difference-cover sample period: 1024 Endianness: little Actual local endianness: little Sanity checking: disabled Assertions: disabled Random seed: 0 Sizeofs: void*:8, int:4, long:8, size_t:8 Input files DNA, FASTA: GCF_000001405.38_GRCh38.p12_genomic.fna Building a SMALL index Reading reference sizes Time reading reference sizes: 00:00:20 Calculating joined length Writing header Reserving space for joined string Joining reference sequences Time to join reference sequences: 00:00:20 bmax according to bmaxDivN setting: 773987710 Using parameters --bmax 580490783 --dcv 1024 Doing ahead-of-time memory usage test Passed! Constructing with these parameters: --bmax 580490783 --dcv 1024 Constructing suffix-array element generator Building DifferenceCoverSample Building sPrime Building sPrimeOrder V-Sorting samples V-Sorting samples time: 00:01:24 Allocating rank array Ranking v-sort output Ranking v-sort output time: 00:00:17 Invoking Larsson-Sadakane on ranks Invoking Larsson-Sadakane on ranks time: 00:00:30 Sanity-checking and returning Building samples Reserving space for 12 sample suffixes Generating random suffixes QSorting 12 sample offsets, eliminating duplicates QSorting sample offsets, eliminating duplicates time: 00:00:00 Multikey QSorting 12 samples (Using difference cover) Multikey QSorting samples time: 00:00:00 Calculating bucket sizes Splitting and merging Splitting and merging time: 00:00:00 Avg bucket size: 3.09595e+09 (target: 580490782) Converting suffix-array elements to index image Allocating ftab, absorbFtab Entering Ebwt loop Getting block 1 of 1 No samples; assembling all-inclusive block Sorting block of length 3095950843 for bucket 1 (Using difference cover)
次に、samtoolsでまとめる。並列指定は-@ スレッド数
samtools view -@ 32 -bS SRR002322.sam > SRR002322.bam
続いて、ソート。並列指定は-@ スレッド
samtools sort SRR002322.bam -o SRR002322.sorted.bam -@ 32
最後にmpileup これは並列指定はできないらしい
samtools mpileup -uf GCF_000001405.38_GRCh38.p12_genomic.fna SRR002322.sorted.bam | bcftools view -Ov - > SR002322.raw.bcf
なお、mpileupも並列実行できるパッケージがあったので、いずれテスト。
Sambamba 0.6.8
「先進ゲノム支援」情報解析講習会のご案内
⇒情報解析講習会ビデオ＜2018年度 情報解析講習会（中級者向け）＞
⇒資料（GitHub）
遺伝研講習会の例では、「TopHat2の後継である」HISAT2を使っている。<-- アメリエフのブログ HISAT2
HISAT2 に曰く
HISAT2 is a fast and sensitive alignment program for mapping next-generation sequencing reads (both DNA and RNA) to a population of human genomes (as well as to a single reference genome). Based on an extension of BWT for graphs ...
http://ccb.jhu.edu/software/hisat2/dl/hisat2-2.1.0-source.zip unzip make
バイナリはmakeしたディレクトリ中にできるので、適宜リンクを張ること。
hisat2の処理は、インデックスを作る(hisat2-build) ⇒ hisat2でマップ の順。
![[Toho University]](image/CLogo_Kihon_process.jpg "[Toho University]")
![[YYLAB]](image/yamanouc2.png "[YYLAB]")
hisat2-build s288c.fna s288c-build.fna
Settings:
Output files: "s288c-build.fna.*.ht2"
Line rate: 6 (line is 64 bytes)
Lines per side: 1 (side is 64 bytes)
Offset rate: 4 (one in 16)
FTable chars: 10
Strings: unpacked
Local offset rate: 3 (one in 8)
Local fTable chars: 6
Local sequence length: 57344
Local sequence overlap between two consecutive indexes: 1024
Endianness: little
Actual local endianness: little
Sanity checking: disabled
Assertions: disabled
Random seed: 0
Sizeofs: void*:8, int:4, long:8, size_t:8
Input files DNA, FASTA:
s288c.fna
Reading reference sizes
Time reading reference sizes: 00:00:00
Calculating joined length
Writing header
Reserving space for joined string
Joining reference sequences
Time to join reference sequences: 00:00:00
Time to read SNPs and splice sites: 00:00:00
Using parameters --bmax 2279457 --dcv 1024
Doing ahead-of-time memory usage test
Passed! Constructing with these parameters: --bmax 2279457 --dcv 1024
Constructing suffix-array element generator
Building DifferenceCoverSample
Building sPrime
Building sPrimeOrder
V-Sorting samples
V-Sorting samples time: 00:00:01
Allocating rank array
Ranking v-sort output
Ranking v-sort output time: 00:00:00
Invoking Larsson-Sadakane on ranks
Invoking Larsson-Sadakane on ranks time: 00:00:00
Sanity-checking and returning
Building samples
Reserving space for 12 sample suffixes
Generating random suffixes
QSorting 12 sample offsets, eliminating duplicates
QSorting sample offsets, eliminating duplicates time: 00:00:00
Multikey QSorting 12 samples
(Using difference cover)
Multikey QSorting samples time: 00:00:00
Calculating bucket sizes
Splitting and merging
Splitting and merging time: 00:00:00
Avg bucket size: 1.73673e+06 (target: 2279456)
Converting suffix-array elements to index image
Allocating ftab, absorbFtab
Entering GFM loop
Getting block 1 of 7
Reserving size (2279457) for bucket 1
Calculating Z arrays for bucket 1
Entering block accumulator loop for bucket 1:
bucket 1: 10%
bucket 1: 20%
bucket 1: 30%
bucket 1: 40%
bucket 1: 50%
bucket 1: 60%
bucket 1: 70%
bucket 1: 80%
bucket 1: 90%
bucket 1: 100%
Sorting block of length 1917483 for bucket 1
(Using difference cover)
Sorting block time: 00:00:01
Returning block of 1917484 for bucket 1
Getting block 2 of 7
Reserving size (2279457) for bucket 2
Calculating Z arrays for bucket 2
Entering block accumulator loop for bucket 2:
bucket 2: 10%
bucket 2: 20%
bucket 2: 30%
bucket 2: 40%
bucket 2: 50%
bucket 2: 60%
bucket 2: 70%
bucket 2: 80%
bucket 2: 90%
bucket 2: 100%
Sorting block of length 1565107 for bucket 2
(Using difference cover)
Sorting block time: 00:00:00
Returning block of 1565108 for bucket 2
Getting block 3 of 7
Reserving size (2279457) for bucket 3
Calculating Z arrays for bucket 3
Entering block accumulator loop for bucket 3:
bucket 3: 10%
bucket 3: 20%
bucket 3: 30%
bucket 3: 40%
bucket 3: 50%
bucket 3: 60%
bucket 3: 70%
bucket 3: 80%
bucket 3: 90%
bucket 3: 100%
Sorting block of length 2077071 for bucket 3
(Using difference cover)
Sorting block time: 00:00:01
Returning block of 2077072 for bucket 3
Getting block 4 of 7
Reserving size (2279457) for bucket 4
Calculating Z arrays for bucket 4
Entering block accumulator loop for bucket 4:
bucket 4: 10%
bucket 4: 20%
bucket 4: 30%
bucket 4: 40%
bucket 4: 50%
bucket 4: 60%
bucket 4: 70%
bucket 4: 80%
bucket 4: 90%
bucket 4: 100%
Sorting block of length 1959963 for bucket 4
(Using difference cover)
Sorting block time: 00:00:00
Returning block of 1959964 for bucket 4
Getting block 5 of 7
Reserving size (2279457) for bucket 5
Calculating Z arrays for bucket 5
Entering block accumulator loop for bucket 5:
bucket 5: 10%
bucket 5: 20%
bucket 5: 30%
bucket 5: 40%
bucket 5: 50%
bucket 5: 60%
bucket 5: 70%
bucket 5: 80%
bucket 5: 90%
bucket 5: 100%
Sorting block of length 1858740 for bucket 5
(Using difference cover)
Sorting block time: 00:00:01
Returning block of 1858741 for bucket 5
Getting block 6 of 7
Reserving size (2279457) for bucket 6
Calculating Z arrays for bucket 6
Entering block accumulator loop for bucket 6:
bucket 6: 10%
bucket 6: 20%
bucket 6: 30%
bucket 6: 40%
bucket 6: 50%
bucket 6: 60%
bucket 6: 70%
bucket 6: 80%
bucket 6: 90%
bucket 6: 100%
Sorting block of length 630502 for bucket 6
(Using difference cover)
Sorting block time: 00:00:00
Returning block of 630503 for bucket 6
Getting block 7 of 7
Reserving size (2279457) for bucket 7
Calculating Z arrays for bucket 7
Entering block accumulator loop for bucket 7:
bucket 7: 10%
bucket 7: 20%
bucket 7: 30%
bucket 7: 40%
bucket 7: 50%
bucket 7: 60%
bucket 7: 70%
bucket 7: 80%
bucket 7: 90%
bucket 7: 100%
Sorting block of length 2148233 for bucket 7
(Using difference cover)
Sorting block time: 00:00:01
Returning block of 2148234 for bucket 7
Exited GFM loop
fchr[A]: 0
fchr[C]: 3766349
fchr[G]: 6086925
fchr[T]: 8404025
fchr[$]: 12157105
Exiting GFM::buildToDisk()
Returning from initFromVector
Wrote 8248454 bytes to primary GFM file: s288c-build.fna.1.ht2
Wrote 3039284 bytes to secondary GFM file: s288c-build.fna.2.ht2
Re-opening _in1 and _in2 as input streams
Returning from GFM constructor
Returning from initFromVector
Wrote 5399069 bytes to primary GFM file: s288c-build.fna.5.ht2
Wrote 3092708 bytes to secondary GFM file: s288c-build.fna.6.ht2
Re-opening _in5 and _in5 as input streams
Returning from HierEbwt constructor
Headers:
len: 12157105
gbwtLen: 12157106
nodes: 12157106
sz: 3039277
gbwtSz: 3039277
lineRate: 6
offRate: 4
offMask: 0xfffffff0
ftabChars: 10
eftabLen: 0
eftabSz: 0
ftabLen: 1048577
ftabSz: 4194308
offsLen: 759820
offsSz: 3039280
lineSz: 64
sideSz: 64
sideGbwtSz: 48
sideGbwtLen: 192
numSides: 63319
numLines: 63319
gbwtTotLen: 4052416
gbwtTotSz: 4052416
reverse: 0
linearFM: Yes
Total time for call to driver() for forward index: 00:00:06
準備終わり。
マッピングは
hisat2 -p 32 --dta-cufflinks -x s288c-build.fna -1 paired_SRR453566_1.trim.fastq -2 paired_SRR453566_2.trim.fastq -S SRR453566.sam
5115482 reads; of these:
5115482 (100.00%) were paired; of these:
323268 (6.32%) aligned concordantly 0 times
4524989 (88.46%) aligned concordantly exactly 1 time
267225 (5.22%) aligned concordantly >1 times
----
323268 pairs aligned concordantly 0 times; of these:
77115 (23.85%) aligned discordantly 1 time
----
246153 pairs aligned 0 times concordantly or discordantly; of these:
492306 mates make up the pairs; of these:
341629 (69.39%) aligned 0 times
135729 (27.57%) aligned exactly 1 time
14948 (3.04%) aligned >1 times
96.66% overall alignment rate
で終わり。
ここで、samtoolsを使ってsamファイルをbamファイルに変換し、かつ、positionでsortする。
samtools/bcftoolsのインストールは、Samtoolsのサイト]http://www.htslib.org/download/からダウンロードし、
./configure make sudo make install
で完了。
samtools viewを使ってsamファイルSRR453566.sam をbamファイル SR453566.bamに変換。@32は32スレッド使用。
samtools view -@ 32 -b SRR453566.sam > SRR453566.bam 又は samtools view -@ 32 -b SRR453566.sam -o SRR453566.bam
samtools sortを使ってbamファイルSRR453566.bam をソートしてSRR453566.sorted.bamに出力
samtools sort -@ 32 SRR453566.bam > SRR453566.sorted.bam [bam_sort_core] merging from 0 files and 32 in-memory blocks...
samtools mpileup -uvf s288c.fna SRR453566.sorted.bam > SRR453566.vcf [warning] samtools mpileup option `u` is functional, but deprecated. Please switch to using bcftools mpileup in future. [warning] samtools mpileup option `v` is functional, but deprecated. Please switch to using bcftools mpileup in future. [mpileup] 1 samples in 1 input files
同じことをbcftoolsでは
bcftools mpileup -Ou -f s288c.fna SRR453566.sorted.bam > SRR453566.vcf
なお、パラメタ --threads 16 などは実際には並列化の効果が無かった。
bcftools mpileup -Ou -f <ref.fa> <sample1.bam> <sample2.bam> <sample3.bam> | bcftools call -vmO z -o <study.vcf.gz>
と書いている。
bcftoolsのマニュアルではviewの代わりにcallを使えとしており、具体的な抽出処理をどうするか例がいくつかある。
更に、 HISAT2では
bcftools view -bvcg SRR453566.vcf > SRR453566.raw.bcf
とするとしているが、パラメタ-bvgfはエラー。
解説を見ると、最近のバージョンではbcftools callによってコールを取出すらしい。
bcftools call -O v -v -c SRR453566.vcf > SRR453566.raw.bcf
が正解の模様。得られた結果は（先頭コメントラインを除くと）
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SRR453566.sorted.bam NC_001133.9 137 . C CT 4.4191 . INDEL;IDV=1;IMF=1;DP=1;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,1,0;MQ=60;FQ=-37.5262 GT:PL 0/1:40,3,0 NC_001133.9 349 . C T 8.64911 . DP=1;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,0,1;MQ=60;FQ=-29.9908 GT:PL 1/1:38,3,0 NC_001133.9 11969 . C G 10.4247 . DP=1;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,1,0;MQ=60;FQ=-29.9905 GT:PL 1/1:40,3,0 NC_001133.9 11997 . T A 27.0206 . DP=2;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,1,0;MQ=60;FQ=-29.9901 GT:PL 1/1:57,3,0 NC_001133.9 12361 . G A 4.13164 . DP=1;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,1,0;MQ=60;FQ=-29.9928 GT:PL 0/1:32,3,0 NC_001133.9 12373 . G A 4.13164 . DP=1;SGB=-0.379885;MQ0F=0;AF1=1;AC1=2;DP4=0,0,1,0;MQ=60;FQ=-29.9928 GT:PL 0/1:32,3,0
といった感じ。INFO部分の解説はヘッダ部分にある通り、
##INFO=<ID=BQB,Number=1,Type=Float,Description="Mann-Whitney U test of Base Quality Bias (bigger is better)"> ##INFO=<ID=MQSB,Number=1,Type=Float,Description="Mann-Whitney U test of Mapping Quality vs Strand Bias (bigger is better)"> ##INFO=<ID=SGB,Number=1,Type=Float,Description="Segregation based metric."> ##INFO=<ID=MQ0F,Number=1,Type=Float,Description="Fraction of MQ0 reads (smaller is better)"> ##FORMAT=<ID=PL,Number=G,Type=Integer,Description="List of Phred-scaled genotype likelihoods"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##INFO=<ID=AF1,Number=1,Type=Float,Description="Max-likelihood estimate of the first ALT allele frequency (assuming HWE)"> ##INFO=<ID=AF2,Number=1,Type=Float,Description="Max-likelihood estimate of the first and second group ALT allele frequency (assuming HWE)"> ##INFO=<ID=AC1,Number=1,Type=Float,Description="Max-likelihood estimate of the first ALT allele count (no HWE assumption)"> ##INFO=<ID=MQ,Number=1,Type=Integer,Description="Root-mean-square mapping quality of covering reads"> ##INFO=<ID=FQ,Number=1,Type=Float,Description="Phred probability of all samples being the same"> ##INFO=<ID=PV4,Number=4,Type=Float,Description="P-values for strand bias, baseQ bias, mapQ bias and tail distance bias"> ##INFO=<ID=G3,Number=3,Type=Float,Description="ML estimate of genotype frequencies"> ##INFO=<ID=HWE,Number=1,Type=Float,Description="Chi^2 based HWE test P-value based on G3"> ##INFO=<ID=DP4,Number=4,Type=Integer,Description="Number of high-quality ref-forward , ref-reverse, alt-forward and alt-reverse bases">
バリアントコール結果のVCFフォーマット - macでインフォマティクス
bcftools mpileupで、複数サンプルを並べてみる。このページ, bcftoolsのページ
bcftools mpileup -Ou -f s288c.fna SRR453566.sorted.bam SRR453567.sorted.bam SRR453568.sorted.bam SRR453569.sorted.bam SRR453570.sorted.bam SRR453571.sorted.bam | bcftools call -Ou -mv -o SRR453566_mpileup.call.vcf
更に結果を読めるようにする。
bcftools view SRR453566_mpileup.call.vcf | vcf-annotate -f + > SRR453566_mpileup.call.annotate.vcf
次のような結果になった。
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SRR453566.sorted.bam SRR453567.sorted.bam SRR453568.sorted.bam SRR453569.sorted.bam SRR453570.sorted.bam SRR453571.sorted.bam NC_001133.9 136 . G A 68 SnpGap DP=4;VDB=0.00992398;SGB=-0.896883;MQ0F=0;AC=8;AN=8;DP4=0,0,4,0;MQ=60 GT:PL 1/1:23,3,0 1/1:24,3,0 ./.:0,0,0 1/1:23,3,0 1/1:23,3,0 ./.:0,0,0 NC_001133.9 137 . C CT 135 PASS INDEL;IDV=1;IMF=1;DP=4;VDB=0.00992398;SGB=-0.896883;MQ0F=0;AC=8;AN=8;DP4=0,0,4,0;MQ=60 GT:PL 1/1:40,3,0 1/1:40,3,0 ./.:0,0,0 1/1:40,3,0 1/1:40,3,0 ./.:0,0,0 NC_001133.9 286 . A T 13.4171 MinAB;MinDP DP=1;SGB=-0.045183;MQ0F=0;AC=2;AN=2;DP4=0,0,1,0;MQ=60 GT:PL ./.:0,0,0 ./.:0,0,0 1/1:38,3,0 ./.:0,0,0 ./.:0,0,0 ./.:0,0,0 NC_001133.9 305 . C G 10.6101 MinAB;MinDP DP=1;SGB=-0.045183;MQ0F=0;AC=2;AN=2;DP4=0,0,1,0;MQ=60 GT:PL ./.:0,0,0 ./.:0,0,0 1/1:35,3,0 ./.:0,0,0 ./.:0,0,0 ./.:0,0,0 NC_001133.9 349 . C T 88 PASS DP=3;VDB=0.102722;SGB=-0.471632;MQ0F=0;AC=6;AN=6;DP4=0,0,0,3;MQ=60 GT:PL 1/1:38,3,0 ./.:0,0,0 1/1:35,3,0 ./.:0,0,0 ./.:0,0,0 1/1:40,3,0 NC_001133.9 373 . T C 9.69791 Qual;MinAB DP=2;SGB=-0.045183;RPB=1;MQB=1;BQB=1;MQ0F=0;ICB=0.5;HOB=0.5;AC=2;AN=4;DP4=0,1,0,1;MQ=60 GT:PL 0/1:0,3,41 ./.:0,0,0 0/1:40,3,0 ./.:0,0,0 ./.:0,0,0 ./.:0,0,0 NC_001133.9 457 . CAAA CAA 7.96917 Qual;MinAB;MinDP INDEL;IDV=1;IMF=1;DP=1;SGB=-0.045183;MQ0F=0;AC=2;AN=2;DP4=0,0,0,1;MQ=60 GT:PL ./.:0,0,0 ./.:0,0,0 ./.:0,0,0 ./.:0,0,0 1/1:32,3,0 ./.:0,0,0
サンプル間で共通する変異と固有の変異を抽出する - macでインフォマティクス
それで、bamファイルや、call抽出した後のvcfファイルに、インデックス付けが必要になることがある。たとえばIGVで表示するときなど。
インデックス付けはbcftoolsで可能だが、入力たるvcfファイルがbgzipされていなければならない。gdzip自体はsamtoolsのhtslibの中に入っていて、samtoolsをmake; make installしただけでは入らない。make all all-htslib; make install install-htslib のように追加makeする必要がある。
bgzip -c SRR453566.raw.bcf > SRR453566.raw.bcf.gz bcftools index SRR453566.raw.bcf.gz
この状態でIGVに食わせることができる。
import igv
# tracksでVCFを指定してみる
ref = {
"id": "Saccha",
"name": "Saccha",
"fastaURL": "files/s288c.fna",
"indexURL": "files/s288c.fna.fai",
"tracks": [
{
"name": "My GFF",
"type": "annotation",
"format": "gff3",
#"displayMode": "EXPANDED",
"url": "files/s288c_e.gff",
"indexed": False,
},
{
"name": "VCF",
"type": "variant",
"format": "vcf",
"displayMode": "EXPANDED",
"url": "files/SRR453566.raw.bcf.gz",
"indexed": True,
}
]
}
b = igv.Browser({"reference": ref})
b.show()
Feature Countの前処理として、GeneIDを付与する。
遺伝研の提供するpythonプログラム add_gene_id で処理。そのために、パッケージbcbio-gffが必要なので、pipでインストール
pip install bcbio-gff
これで、add_gene_idを稼働できる。
python add_gene_id s288c.gff s288c_e.gff
得られたs288c.e.gffにはgene_idが入っているはず。
これをFeature Countする。biopapyrusの説明 を引用すると：
featureCounts はマッピング結果である BAM または SAM ファイルを入力とし、各 feature 領域（gene、CDS、exon）ごとにリードをカウントするプログラムである。featureCounts は Subread パッケージに含まれるプログラムの一つである。featureCounts を利用するには subread をインストールする必要がある。 subread のソースコードとコンパイルされたプログラムは sourceforge で公開されている。必要に応じてどちらかをダウンロードして使用する。
ということで、subreadのページからダウンロード。
featureCounts -p -T 8 -t exon -g gene_id -a $GFF \ -o ../featurecount/counts.txt \ SRR453566.sorted.bam \ SRR453567.sorted.bam \ SRR453568.sorted.bam \ SRR453569.sorted.bam \ SRR453570.sorted.bam \ SRR453571.sorted.bam
$GFFは参照シーケンスのGene情報を含むファイル。featureCount (Subread) のマニュアルでは
The annotation file should be in either GTF format or a simplified annotation format (SAF) as shown below (columns are tab-delimited): GeneID Chr Start End Strand 497097 chr1 3204563 3207049 - 497097 chr1 3411783 3411982 - 497097 chr1 3660633 3661579 - ...
としてあり、GTFフォーマットはFrequently Asked Questions: Data File FormatsおよびGTF2.2: A Gene Annotation Format (Revised Ensembl GTF)によると
GTF stands for Gene transfer format. It borrows from GFF, but has additional structure that warrants a separate definition and format name. Structure is as GFF, so the fields are: <seqname> <source> <feature> <start> <end> <score> <strand> <frame> [attributes] [comments] Here is a simple example with 3 translated exons. Order of rows is not important. 381 Twinscan CDS 380 401 . + 0 gene_id "001"; transcript_id "001.1"; 381 Twinscan CDS 501 650 . + 2 gene_id "001"; transcript_id "001.1"; 381 Twinscan CDS 700 707 . + 2 gene_id "001"; transcript_id "001.1"; 381 Twinscan start_codon 380 382 . + 0 gene_id "001"; transcript_id "001.1"; 381 Twinscan stop_codon 708 710 . + 0 gene_id "001"; transcript_id "001.1"; The whitespace in this example is provided only for readability. In GTF, fields must be separated by a single TAB and no white space.
更に、GFFフォーマットはFrequently Asked Questions: Data File Formatsによると
GFF (General Feature Format) lines are based on the Sanger GFF2 specification. GFF lines have nine required fields that must be tab-separated. If the fields are separated by spaces instead of tabs, the track will not display correctly. For more information on GFF format, refer to Sanger's GFF page.
また、上記で参照しているSangerのGFF2/3の説明は、GFF2とGFF3にある。GFF2はobsoleteとされ、GFF3がrecommendされている。更に、GFF3ではファイルの最後にオプションでsequenceが書ける。
動作することのテストとして、SRR453566.sorted.bamの１つだけを処理してみる。
featureCounts -p -T 32 -t exon -g gene_id -a s288c_e.gff -o SRR435366.counts.txt SRR453566.sorted.bam
========== _____ _ _ ____ _____ ______ _____
===== / ____| | | | _ \| __ \| ____| /\ | __ \
===== | (___ | | | | |_) | |__) | |__ / \ | | | |
==== \___ \| | | | _ <| _ /| __| / /\ \ | | | |
==== ____) | |__| | |_) | | \ \| |____ / ____ \| |__| |
========== |_____/ \____/|____/|_| \_\______/_/ \_\_____/
v1.6.3
//========================== featureCounts setting ===========================\\ || || || Input files : 1 BAM file || || P SRR453566.sorted.bam || || || || Output file : SRR435366.counts.txt || || Summary : SRR435366.counts.txt.summary || || Annotation : s288c_e.gff (GTF) || || Dir for temp files : ./ || || || || Threads : 32 || || Level : meta-feature level || || Paired-end : yes || || Multimapping reads : not counted || || Multi-overlapping reads : not counted || || Min overlapping bases : 1 || || || || Chimeric reads : counted || || Both ends mapped : not required || || || \\===================== http://subread.sourceforge.net/ ======================// //================================= Running ==================================\\ || || || Load annotation file s288c_e.gff ... || || Features : 6761 || || Meta-features : 6420 || || Chromosomes/contigs : 17 || || || || Process BAM file SRR453566.sorted.bam... || || Paired-end reads are included. || || Assign alignments (paired-end) to features... || || || || WARNING: reads from the same pair were found not adjacent to each || || other in the input (due to read sorting by location or || || reporting of multi-mapping read pairs). || || || || Pairing up the read pairs. || || || || Total alignments : 5650830 || || Successfully assigned alignments : 4568783 (80.9%) || || Running time : 0.03 minutes || || || || || || Summary of counting results can be found in file "SRR435366.counts.txt.su || || mmary" || || || \\===================== http://subread.sourceforge.net/ ======================//
出力は（先頭）
# Program:featureCounts v1.6.3; Command:"featureCounts" "-p" "-T" "32" "-t" "exon" "-g" "gene_id" "-a" "s288c_e.gff" "-o" "SRR435366.counts.txt" "SRR453566.sorted.bam" Geneid Chr Start End Strand Length SRR453566.sorted.bam gene_0001 NC_001133.9 1807 2169 - 363 0 gene_0002 NC_001133.9 2480 2707 + 228 0 gene_0003 NC_001133.9 7235 9016 - 1782 0 gene_0004 NC_001133.9 11565 11951 - 387 0 gene_0005 NC_001133.9 12046 12426 + 381 2 gene_0006 NC_001133.9 13363 13743 - 381 0 gene_0007 NC_001133.9 21566 21850 + 285 0 gene_0008 NC_001133.9 22395 22685 - 291 0 gene_0009 NC_001133.9 24000 27968 - 3969 32 gene_0010 NC_001133.9 31567 32940 + 1374 65 gene_0011 NC_001133.9 33448 34701 + 1254 119 gene_0012 NC_001133.9 35155 36303 + 1149 1155
＞＞遺伝研セミナーのこの先の処理はRNAseq��-0（遺伝研セミナーでの）発現解析〜カウントデータの解析
Johns Hopkinsから論文 Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols volume 11, pages 1650–1667 (2016)
それで、HISAT2でリードをアセンブルした結果のbamファイルをStringTieでアセンブルする。
StringTieのインストールは、マニュアル通り。ソースをダウンロードし、make。インストールは自分で/usr/local/bin/stringtieからリンクを張れば終わり。
処理は、あらかじめHISAT2でsorted.bamができているとして、（HISAT2で--dtaを指定すること）
stringtie -B -p 16 -o SRR453566.gtf -G s288c_e.gff SRR453566.sorted.bam
ここで、-B は出力をBallgownで使うための設定、-G s288c_e.gff はreference annotation file (in GTF or GFF3 format)、-p 16はスレッド数で、出力は-o SRR453566.gtf で指定。
e2t.ctab
e_data.ctab
i2t.ctab
i_data.ctab
t_data.ctab
であり、これをBallgawnに食わせる必要がある。このstringtieの出力を、Ballgawnで整理して可視化する。
A program for computing differentially expressed genes in two or more RNA-seq experiments, using the output of StringTie or Cufflinks. The Ballgown package provides functions to organize, visualize, and analyze expression measurements. Ballgown is written in R and is part of Bioconductor.
GitHub - alyssafrazee/ballgown: Bioconductor package "ballgown"
HISAT-StringTie-Ballgown を試してみよう - Palmsonntagmorgen これでは論文ではoptionalとなっていますが、gffcompareを使って、生成された遺伝子リストとreferenceの遺伝子リストを比較することができます。
続きとしてHISAT-StringTie-Ballgown を試してみよう その2 - Palmsonntagmorgen
