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这个转录组比对工具很快，十几分钟一个样品

發布時間：2025/3/15 编程问答 30 豆豆

生活随笔收集整理的這篇文章主要介紹了这个转录组比对工具很快，十几分钟一个样品小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

前面我們做了STAR基因組索引構建所需資源的評估，現在我們看下reads比對對計算資源和時間的需求。

下載原始測序數據

首先下載獲得樣品SRR1039517的原始測序數據，數據量約為34 million長度為63個堿基的雙端reads, 總堿基數 4.3 G左右。具體見NGS基礎：測序原始數據批量下載。

fastq-dump -v --split-3 --gzip SRR1039517seqkit stats SRR1039517_1.fastq.gz SRR1039517_1.fastq.gz file format type num_seqs sum_len min_len avg_len max_len SRR1039517_1.fastq.gz FASTQ DNA 34,298,260 2,160,790,380 63 63 63 SRR1039517_1.fastq.gz FASTQ DNA 34,298,260 2,160,790,380 63 63 63

序列拆分為小文件，模擬不同測序量

把所有序列拆分為最多包含20萬條reads的小的原始測序數據文件。并重命名，去掉文件名前面填充的0。主要是便于后面進行批量處理。

seqkit split2 -s 200000 -1 SRR1039517_1.fastq.gz -2 SRR1039517_2.fastq.gz# 如果是ubuntu，可能需要使用rename.ul rename '_00' '_' SRR1039517_1.fastq.gz.split/* rename '_0' '_' SRR1039517_1.fastq.gz.split/*

不同測序量的樣品比對回基因組

拆分后，總共獲得172對測序數據。我們通過cat命令對序列文件進行累加，模擬不同測序深度的樣品，并進行比對，統計每次比對需要的計算資源和時間資源。這里使用了4個線程對每個樣品并行比對，不同樣品是串行比對。

/bin/rm -f SRR1039517_1.part_1.fastq.gz SRR1039517_2.part_2.fastq.gz for part in `seq 1 172`; docat SRR1039517_1.fastq.gz.split/SRR1039517_1.part_${part}.fastq.gz >>SRR1039517_1.part_1.fastq.gzcat SRR1039517_1.fastq.gz.split/SRR1039517_2.part_${part}.fastq.gz >>SRR1039517_2.part_2.fastq.gzi=SRR1039517mkdir -p ${i}.${part}mkdir -p tmp/usr/bin/time -v -o star.${i}.${part}.log STAR --runMode alignReads --runThreadN 4 \--readFilesIn ${i}_1.part_1.fastq.gz ${i}_2.part_2.fastq.gz \--readFilesCommand zcat --genomeDir star_GRCh38 \--outFileNamePrefix ${i}.${part}/${i}. --outFilterType BySJout --outSAMattributes NH HI AS NM MD \--outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 \--alignIntronMin 20 --alignIntronMax 1000000 \--alignMatesGapMax 1000000 \--outFilterMatchNminOverLread 0.66 --outFilterScoreMinOverLread 0.66 \--winAnchorMultimapNmax 70 --seedSearchStartLmax 45 \--outSAMattrIHstart 0 --outSAMstrandField intronMotif \--genomeLoad LoadAndKeep \--outTmpDir /tmp/${i}.${part}/ \--outSAMtype BAM Unsorted --quantMode GeneCounts done

整理資源利用信息

重新整理一個通用的awk腳本，命名為timeIntegrate2.awk：

#!/usr/bin/awk -ffunction to_minutes(time_str) {a=split(time_str,array1,":"); minutes=0; count=1; for(i=a;i>=1;--i) {minutes+=array1[count]*60^(i-2);count+=1;}return minutes;} BEGIN{OFS="\t"; FS=": "; }ARGIND==1{if(FNR==1) header=$0; else datasize=$0;}ARGIND==2{if(FNR==1 && outputHeader==1) print "Time_cost\tMemory_cost\tnCPU", header;if($1~/Elapsed/) {time_cost=to_minutes($2);} else if($1~/Maximum resident set size/){memory_cost=$2/10^6;} else if($1~/CPU/) {cpu=($2+0)/100}; }END{print time_cost, memory_cost, cpu, datasize}

具體整合代碼

i=SRR1039517 /bin/rm -f ${i}_1.part_1.fastq.gz ${i}_2.part_2.fastq.gz GRCh38_39517_star_reads_map.summary for part in `seq 1 172`; docat ${i}_1.fastq.gz.split/${i}_1.part_${part}.fastq.gz >>${i}_1.part_1.fastq.gzcat ${i}_1.fastq.gz.split/${i}_2.part_${part}.fastq.gz >>${i}_2.part_2.fastq.gz~/soft/seqkit stats -j 8 ${i}_1.part_1.fastq.gz ${i}_2.part_2.fastq.gz | sed 's/,//g' | \awk 'BEGIN{OFS="\t"}{reads+=$4; bases+=$5}END{print "nReads\tnBases"; print reads/10^6, bases/10^9}' | \awk -v outputHeader=${part} -f ./timeIntegrate2.awk - star.${i}.${part}.log \>>GRCh38_39517_star_reads_map.summarydone /bin/rm -f ${i}_1.part_1.fastq.gz ${i}_2.part_2.fastq.gzpaste <(for part in `seq 1 172`; do du -s ${i}.${part} | \awk -v i=${part} '{if(i==1) print "outputSize"; print $1/10^6}'; done) \GRCh38_39517_star_reads_map.summary >GRCh38_39517_star_reads_map.summary2

結果如下

outputSize (G) Time_cost (minutes) Memory_cost (Gb) nCPU nReads (million) nBases (Gb) 0.034332 0.28 9.9072 1.9 0.4 0.0252 0.067064 0.228 13.2356 2.28 0.8 0.0504 0.099504 0.340167 15.5853 1.98 1.2 0.0756 0.131672 0.326 17.3644 2.55 1.6 0.1008 0.164156 0.413 18.7912 2.43 2 0.126 0.196804 0.463167 19.969 2.52 2.4 0.1512 0.229112 0.4615 20.939 2.88 2.8 0.1764 0.261368 0.516667 21.7589 2.83 3.2 0.2016 0.294484 0.568 22.5243 2.89 3.6 0.2268 0.32744 0.586667 23.182 3.06 4 0.252 0.359588 0.601 23.7126 3.26 4.4 0.2772 0.391772 0.683333 24.1722 3.09 4.8 0.3024 0.424232 0.826167 24.595 2.98 5.2 0.3276 0.456388 0.854333 24.9512 3.28 5.6 0.3528 0.488488 0.917167 25.2751 3.29 6 0.378 0.520716 0.937 25.5713 3.33 6.4 0.4032 0.552924 0.999167 25.8344 3.32 6.8 0.4284 0.584712 1.0485 26.0817 3.37 7.2 0.4536 0.616944 1.0965 26.2919 3.39 7.6 0.4788 0.64944 1.135 26.512 3.45 8 0.504 0.681984 1.17517 26.6706 3.49 8.4 0.5292 0.714948 1.14683 26.8639 3.52 8.8 0.5544 0.748312 1.10883 27.0044 3.47 9.2 0.5796 0.780928 1.14 27.1747 3.55 9.6 0.6048 0.8138 1.26317 27.2956 3.5 10 0.63 0.846996 1.3 27.4216 3.54 10.4 0.6552 0.880352 1.486 27.5397 3.59 10.8 0.6804 0.91364 1.49983 27.6463 3.6 11.2 0.7056 0.946512 1.55083 27.7506 3.53 11.6 0.7308 0.979624 1.61967 27.8361 3.49 12 0.756 1.01286 1.62517 27.943 3.57 12.4 0.7812 1.04676 1.66767 28.0145 3.59 12.8 0.8064 1.08059 1.6755 28.1329 3.63 13.2 0.8316 1.11306 1.7145 28.1842 3.63 13.6 0.8568 1.14475 1.7735 28.2665 3.65 14 0.882 1.17665 1.89567 28.308 3.62 14.4 0.9072 1.20814 1.96067 28.3619 3.55 14.8 0.9324 1.23969 1.948 28.4374 3.67 15.2 0.9576 1.27152 2.00583 28.4658 3.69 15.6 0.9828 1.30336 2.05633 28.5352 3.65 16 1.008 1.33483 2.04983 28.5566 3.68 16.4 1.0332 1.36708 2.1245 28.6243 3.65 16.8 1.0584 1.39974 2.25417 28.6217 3.67 17.2 1.0836 1.43171 2.27817 28.7085 3.72 17.6 1.1088 1.46321 2.322 28.7688 3.71 18 1.134 1.49494 2.282 28.8025 3.71 18.4 1.1592 1.5268 2.3305 28.8338 3.72 18.8 1.1844 1.55854 2.36067 28.8627 3.73 19.2 1.2096 1.59012 2.412 28.89 3.68 19.6 1.2348 1.62192 2.43117 28.917 3.69 20 1.26 1.65361 2.49833 28.9436 3.68 20.4 1.2852 1.68481 2.56833 28.9686 3.7 20.8 1.3104 1.71662 2.58367 28.9926 3.74 21.2 1.3356 1.74859 2.64333 29.0157 3.75 21.6 1.3608 1.78063 2.72033 29.0376 3.71 22 1.386 1.81293 2.7445 29.0589 3.74 22.4 1.4112 1.84533 2.79183 29.0792 3.75 22.8 1.4364 1.87753 2.7765 29.0982 3.74 23.2 1.4616 1.91008 2.86767 29.1176 3.72 23.6 1.4868 1.94256 3.00517 29.1356 3.75 24 1.512 1.97522 3.06933 29.1535 3.72 24.4 1.5372 2.00784 3.06633 29.1702 3.72 24.8 1.5624 2.04029 3.083 29.1852 3.64 25.2 1.5876 2.07288 3.0285 29.2007 3.77 25.6 1.6128 2.1055 3.049 29.2164 3.78 26 1.638 2.13861 3.08583 29.2336 3.74 26.4 1.6632 2.1727 3.18417 29.2544 3.75 26.8 1.6884 2.20504 3.22867 29.2682 3.78 27.2 1.7136 2.23734 3.35617 29.2812 3.72 27.6 1.7388 2.26982 3.3635 29.2938 3.78 28 1.764 2.30174 3.38567 29.3051 3.8 28.4 1.7892 2.33385 3.45417 29.316 3.79 28.8 1.8144 2.36633 3.49933 29.3272 3.78 29.2 1.8396 2.3987 3.44333 29.3379 3.78 29.6 1.8648 2.43073 3.545 29.3478 3.81 30 1.89 2.46358 3.74117 29.3585 3.79 30.4 1.9152 2.49704 3.79167 29.3694 3.81 30.8 1.9404 2.52971 3.68567 29.3786 3.82 31.2 1.9656 2.56169 3.7315 29.3871 3.78 31.6 1.9908 2.59398 3.78933 29.3955 3.77 32 2.016 2.62646 3.748 29.4036 3.78 32.4 2.0412 2.65862 3.76967 29.4113 3.81 32.8 2.0664 2.69066 3.92767 29.4191 3.8 33.2 2.0916 2.72278 3.95983 29.4265 3.81 33.6 2.1168 2.75499 4.024 29.4338 3.8 34 2.142 2.78692 4.05083 29.4403 3.82 34.4 2.1672 2.81906 4.1095 29.447 3.81 34.8 2.1924 2.8514 4.18167 29.4532 3.78 35.2 2.2176 2.88368 4.09467 29.4598 3.81 35.6 2.2428 2.91615 4.25317 29.4661 3.78 36 2.268 2.94896 4.43683 29.4726 3.81 36.4 2.2932 2.98138 4.49333 29.4781 3.79 36.8 2.3184 3.01411 4.38067 29.4837 3.77 37.2 2.3436 3.04697 4.34867 29.4898 3.82 37.6 2.3688 3.0799 4.382 29.4955 3.83 38 2.394 3.11275 4.35467 29.5012 3.81 38.4 2.4192 3.14541 4.9245 29.5065 3.42 38.8 2.4444 3.17823 4.62317 29.5116 3.77 39.2 2.4696 3.21122 4.63733 29.5172 3.82 39.6 2.4948 3.24487 4.68467 29.5231 3.83 40 2.52 3.27845 4.711 29.5285 3.85 40.4 2.5452 3.31161 4.8395 29.5348 3.8 40.8 2.5704 3.3444 4.7865 29.5413 3.82 41.2 2.5956 3.37759 4.883 29.5497 3.81 41.6 2.6208 3.41041 5.14933 29.5569 3.83 42 2.646 3.44278 5.204 29.5618 3.85 42.4 2.6712 3.47552 5.124 29.5659 3.8 42.8 2.6964 3.5085 5.10783 29.5709 3.81 43.2 2.7216 3.54084 5.109 29.5752 3.84 43.6 2.7468 3.57301 5.12683 29.5787 3.78 44 2.772 3.60639 5.23267 29.5826 3.82 44.4 2.7972 3.63972 5.61417 29.5867 3.64 44.8 2.8224 3.67239 6.52033 29.5927 3.48 45.2 2.8476 3.70516 7.05183 29.5007 3.42 45.6 2.8728 3.73857 6.949 29.6024 3.48 46 2.898 3.77162 6.888 29.6069 3.45 46.4 2.9232 3.80419 5.52133 29.6101 3.87 46.8 2.9484 3.83614 5.8575 29.6128 3.84 47.2 2.9736 3.86866 5.91483 29.6156 3.85 47.6 2.9988 3.90138 5.78217 29.6197 3.8 48 3.024 3.93331 5.74883 29.6226 3.84 48.4 3.0492 3.96561 5.77317 29.6252 3.83 48.8 3.0744 3.99835 5.76267 29.6295 3.83 49.2 3.0996 4.03138 5.97367 29.6337 3.82 49.6 3.1248 4.06518 8.01017 29.3961 3.38 50 3.15 4.09969 8.138 29.6426 3.38 50.4 3.1752 4.13252 9.755 29.4349 3.26 50.8 3.2004 4.1656 6.49233 29.5448 3.63 51.2 3.2256 4.19947 8.876 29.4903 3.37 51.6 3.2508 4.23331 6.5035 29.6538 3.64 52 3.276 4.26617 6.48483 29.6557 3.84 52.4 3.3012 4.29916 6.68383 29.6583 3.84 52.8 3.3264 4.33211 6.51417 29.6607 3.84 53.2 3.3516 4.36514 6.37817 29.6629 3.86 53.6 3.3768 4.39864 6.481 29.6653 3.81 54 3.402 4.4327 6.43167 29.6689 3.84 54.4 3.4272 4.46627 8.351 29.6731 3.01 54.8 3.4524 4.49779 7.4865 29.6494 3.6 55.2 3.4776 4.52981 10.1942 29.4533 3.19 55.6 3.5028 4.56185 10.7782 29.2792 3.19 56 3.528 4.59322 9.7155 29.3352 3.38 56.4 3.5532 4.62515 9.30333 29.5208 3.41 56.8 3.5784 4.65749 7.293 29.6829 3.56 57.2 3.6036 4.68936 6.48533 29.6845 3.85 57.6 3.6288 4.72118 6.53733 29.686 3.84 58 3.654 4.75413 6.69733 29.6876 3.84 58.4 3.6792 4.78725 6.46033 29.6895 3.84 58.8 3.7044 4.81937 6.389 29.6908 3.81 59.2 3.7296 4.85105 6.33033 29.6923 3.84 59.6 3.7548 4.88335 9.42183 29.6938 2.62 60 3.78 4.91568 7.21483 29.4664 3.54 60.4 3.8052 4.94768 6.4205 29.6966 3.78 60.8 3.8304 4.97946 6.337 29.6978 3.85 61.2 3.8556 5.01139 7.06983 29.699 3.86 61.6 3.8808 5.04355 7.15017 29.7001 3.86 62 3.906 5.07542 7.215 29.7013 3.85 62.4 3.9312 5.10747 7.25917 29.7027 3.85 62.8 3.9564 5.13966 7.27783 29.704 3.86 63.2 3.9816 5.17208 7.3745 29.7054 3.83 63.6 4.0068 5.20458 7.33233 29.707 3.86 64 4.032 5.23759 7.41067 29.7087 3.85 64.4 4.0572 5.27022 7.46467 29.71 3.87 64.8 4.0824 5.30309 7.49667 29.7112 3.86 65.2 4.1076 5.33602 7.554 29.7125 3.86 65.6 4.1328 5.36921 7.5765 29.7138 3.88 66 4.158 5.4022 7.25533 29.7152 3.86 66.4 4.1832 5.43503 6.9645 29.7164 3.85 66.8 4.2084 5.468 6.99817 29.7176 3.87 67.2 4.2336 5.50081 7.01467 29.719 3.87 67.6 4.2588 5.53405 7.02883 29.7202 3.86 68 4.284 5.56759 7.152 29.7214 3.85 68.4 4.3092 5.58427 7.089 29.7221 3.85 68.5965 4.32158

STAR比對的時間隨數據量的變化

在數據量少于50 Million或3 Gb時，比對時間與數據量近乎完美正相關

在數據了更多時，比對時間波動性大，趨勢不明顯。

總體時間差別不大，單個樣品在十幾分鐘內就可以完成。

library(ImageGP) library(ggplot2) library(patchwork)p1 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nReads",yvariable = "Time_cost", smooth_method = "auto",x_label ="Sequencing reads (Million)", y_label = "Running time (minutes)") +scale_x_continuous(breaks=seq(0,70, by=5)) +scale_y_continuous(breaks=seq(1,12,length=12)) p2 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nBases",yvariable = "Time_cost", smooth_method = "auto",x_label ="Sequencing reads (Gb)", y_label = "Running time (minutes)") +scale_x_continuous(breaks=seq(0.5,5, by=0.5)) +scale_y_continuous(breaks=seq(1,12,length=12)) p1 + p2

STAR比對所需內存隨數據量的變化

在測序量小于20 Milion (1.2 G)時，STAR比對所需內存隨測序量增加快速增加，從9.9 G快速增到28 G。

測序量再增加時，STAR比對所需內存變化不大，穩定在30 G以內。

p1 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nReads",yvariable = "Memory_cost", smooth_method = "auto",x_label ="Sequencing reads (Million)", y_label = "Maximum physical memory required (Gb)") +scale_x_continuous(breaks=seq(0,70, by=5)) +scale_y_continuous(breaks=seq(9,30,length=22)) p2 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nBases",yvariable = "Memory_cost", smooth_method = "auto",x_label ="Sequencing reads (Gb)", y_label = "Maximum physical memory required (Gb)") +scale_x_continuous(breaks=seq(0.5,5, by=0.5)) +scale_y_continuous(breaks=seq(9,30,length=22)) p1 + p2

STAR比對時對 CPU 的利用率

提供了4個線程，不是所有階段都能用滿。

數據量越大，CPU利用效率也越高。

后面再測試不同線程數對比對的影響。

CPU利用率降低的數據量部分看上去跟程序運行時間異常的部分一致。

推測是這時硬盤讀寫繁忙，導致時間增加，CPU利用效率降低。

從下面這張圖可以看出，比對時間異常的樣本，它們的CPU利用率也相應的低，但沒有太明顯規律性。推測這部分程序運行時可能受到了其它程序對硬盤讀寫的影響。

p1 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nReads",yvariable = "nCPU", smooth_method = "auto",x_label ="Sequencing reads (Million)", y_label = "Number of CPUs used") +scale_x_continuous(breaks=seq(0,70, by=5)) +scale_y_continuous(breaks=seq(1,4.5,by=0.5), limits=c(1.5,4)) p2 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nBases",yvariable = "nCPU", smooth_method = "auto",x_label ="Sequencing reads (Gb)", y_label = "Number of CPUs used") +scale_x_continuous(breaks=seq(0.5,5, by=0.5)) +scale_y_continuous(breaks=seq(1,4.5,by=0.5),limits=c(1.5,4)) p1 + p2

STAR比對結果文件隨數據量的變化

完美正相關，數據量越大，生成的結果文件也越大。

p1 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nReads",yvariable = "outputSize", smooth_method = "auto",x_label ="Sequencing reads (Million)", y_label = "Disk space usages (Gb)") +scale_x_continuous(breaks=seq(0,70, by=5)) +scale_y_continuous(breaks=seq(0,6,by=0.5), limits=c(0,6)) p2 <- sp_scatterplot("GRCh38_39517_star_reads_map.summary2", melted = T, xvariable = "nBases",yvariable = "outputSize", smooth_method = "auto",x_label ="Sequencing reads (Gb)", y_label = "Disk space usages (Gb)") +scale_x_continuous(breaks=seq(0.5,5, by=0.5)) +scale_y_continuous(breaks=seq(0,6,by=0.5), limits=c(0,6)) p1 + p2

未完待續

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