宏转录组学习笔记（二）
上次竟然忘记把学习的教程地址放上了，这里首先放上：2018-cicese-metatranscriptomics
继续前面的学习，前面已经把软件安装完成，数据库准备好，下面就是分析的过程了，基本上按照原文的命令进行的，由于教程中没有给出tara_f135_full_megahit.fasta这个文件，这里我们就把这几个样本的组装提到了前面，自己组装获得这个序列，然后再进行物种注释。
质控
把不合格的数据去除，这里使用了fastqc对每个样本进行检查，multiqc汇兑结果，然后Trimmomatic进行去除。
1.fastqc检查，multiqc汇兑结果
#激活工作环境
conda activate tera
#为了方便，设置变量，临时有用，退出失效，所以再运行一次
export PROJECT=~/work
#进入文件夹
cd ${PROJECT}
#建立质控文件夹并进入，每个步骤建立文件夹是个好习惯
mkdir -p quality
cd quality
#软链接数据，省去路径问题，也是一个好方法
ln -s ../data/*.fq.gz ./
#再次check一切就绪
printf "I see $(ls -1 *.fq.gz | wc -l) files here.\n"
#开始质控，原文没有加入多线程， 这里我使用4线程
fastqc *.fq.gz -t 4
#查看确认运行完成，一般没有问题
ls -d *fastqc.zip*
'''
TARA_135_DCM_5-20_rep1_1m_1_fastqc.zip TARA_135_SRF_5-20_rep2_1m_1_fastqc.zip
...
TARA_135_SRF_5-20_rep1_1m_2_fastqc.zip TARA_136_SRF_5-20_rep2_1m_2_fastqc.zip
'''
#multiqc汇兑结果
multiqc .
运行完成后使用浏览器查看下结果如TARA_135_SRF_5-20_rep1_1m_1_fastqc.html，就是我们熟悉的网页了。
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2.根据质量修剪和过滤
#建立新的文件夹，软链文件，准备adapters
cd $PROJECT
mkdir -p trim
cd trim
ln -s ../data/*.fq.gz .
cat ~/Miniconda/envs/tera/share/trimmomatic-*/adapters/* > combined.fa
#一个大的for循环解决问题
for filename in *1.fq.gz;
do
#使用 basename 变量去除 _1.fq.gz 产生 base变量，第一次见这种操作
base=$(basename $filename _1.fq.gz)
echo $base
# 运行 Trimmomatic
trimmomatic PE ${base}_1.fq.gz \
${base}_2.fq.gz \
${base}_1.qc.fq.gz s1_se \
${base}_2.qc.fq.gz s2_se \
ILLUMINACLIP:combined.fa:2:40:15 \
LEADING:2 TRAILING:2 \
SLIDINGWINDOW:4:2 \
MINLEN:25
# 保存orphans序列
gzip -9c s1_se s2_se >> orphans.qc.fq.gz
rm -f s1_se s2_se
done
#看下质控效果，同样是fastqc-multiqc，一起完成,稍微看了眼，只去掉了很少序列数
fastqc *.qc.fq.gz -t 4
multiqc .
#取消目录下的所有文件可写权限
chmod a-w ${PROJECT}/trim/*.qc.fq.gz
更多质控trim策略看这， MacManes, 2014 ，这是转录组的教程，但是原理应该通用。
Khmer错误修剪
前面的trimmomatic修剪后，依然有序列包含错误，Q30意味着1000个Q值是30的碱基中，约有一个是错的。高质量碱基也会小概率的错误，反过来，许多低质量碱基也是正确的。然后，我们修剪地很轻微，只去除了质量极低的碱基。这是有意为之，因为组装需要保留尽可能大的测序深度，组装软件可以通过测序深度判断正确与否。另一个修剪选择是基于k-mer丰度（kmer spectral error），文章显示结果优于基于质量修剪。
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它的逻辑是这样的：如果你在高测序深度发现低丰度的K-mers，这些很可能就是错误。当然，菌株变异也会引起这样。在宏转录组中可能遇到极低丰度的菌的情况，所以我们不必须做这一步。我们实验室开发了一个方法，把序列按丰度高低分成两个组，仅修剪高丰度的序列，这样可以兼得低丰度序列，又能去除错误。
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#建立文件夹并进入
cd ${PROJECT}
mkdir -p khmer_trim
cd khmer_trim
#选择一个样本开始，链接文件
ln -s ${PROJECT}/trim/TARA_135_SRF_5-20_*qc.fq.gz ./
#还是for循环运行，这一步是最耗内存和时间的，运行需要8G以上内存，否则会使用swap，运行速度变得超慢
#实测在8G内存上，如果有图形界面的系统会卡死，开的无图形界面的去服务器使用内存7.8G
#参数里已经设置了内存8G,可能改小点也能运行，速度差别，没有测试
for filename in *_1.qc.fq.gz
do
#Use the program basename to remove _1.qc.fq.gz to generate the base
base=$(basename $filename _1.qc.fq.gz)
echo $base
interleave-reads.py ${base}_1.qc.fq.gz ${base}_2.qc.fq.gz | \
trim-low-abund.py - -V -Z 10 -C 3 -o - --gzip -M 8e9 | \
extract-paired-reads.py --gzip -p ${base}.khmer.pe.fq.gz -s ${base}.khmer.se.fq.gz
done
'''
removing ./tmph8zrazrjkhmer/-.pass2
removing temp directory & contents (./tmph8zrazrjkhmer)
read 1995938 reads, 194677930 bp
wrote 1980263 reads, 192516464 bp
looked at 1910590 reads twice (1.96 passes)
removed 15675 reads and trimmed 36041 reads (2.59%)
trimmed or removed 1.11%% of bases (2161466 total)
1995938 reads were high coverage (100.00%);
skipped 0 reads/0 bases because of low coverage
fp rate estimated to be 0.000
output streamed to stdout
DONE; read 1980263 sequences, 982692 pairs and 14879 singletons
wrote to: TARA_135_SRF_5-20_rep2_1m.khmer.pe.fq.gz and TARA_135_SRF_5-20_rep2_1m.khmer.se.fq.gz
'''
#查看k-mer丰度变化,去除了少部分序列（几万条）
unique-kmers.py TARA_135_SRF_5-20_rep1_1m_1.qc.fq.gz TARA_135_SRF_5-20_rep1_1m_2.qc.fq.gz
'''
Estimated number of unique 32-mers in TARA_135_SRF_5-20_rep1_1m_1.qc.fq.gz: 58871706
Estimated number of unique 32-mers in TARA_135_SRF_5-20_rep1_1m_2.qc.fq.gz: 57626073
Total estimated number of unique 32-mers: 100687408
'''
unique-kmers.py TARA_135_SRF_5-20_rep1_1m.khmer.pe.fq.gz
'''
Estimated number of unique 32-mers in TARA_135_SRF_5-20_rep1_1m.khmer.pe.fq.gz: 100296061
Total estimated number of unique 32-mers: 100296061
'''
因为这是专门用来培训的小数据，如果真实数据去除的序列数应该比这个大得多。这里有更多相关知识。
宏转录组组装
基本过程就是你丢给软件一些reads，获得一些代表你的转录组的contigs，或者拉长没有太多明显多态性和长重复的reads。你使用修剪好的reads运行一个转录组组装程序，得到一些组装好的RNA。这些contigs代表环境中真核生物中的转录本（Poly-A mRNA）。
#建立文件夹，链接文件
cd $PROJECT
mkdir -p assembly
cd assembly
ln -fs ${PROJECT}/khmer_trim/*.khmer.pe.fq.gz .
ls
#组装，这里我采用12G内存，2线程，已经是我的笔记本的极限，实际运行下来只使用了几百M的样子
time megahit --12 TARA_135_SRF_5-20_rep1_1m.khmer.pe.fq.gz,TARA_135_SRF_5-20_rep2_1m.khmer.pe.fq.gz --memory 12e9 --num-cpu-threads 4 --out-prefix TARA_135_SRF --out-dir ./TARA_135_SRF_khmer -f
详细输出如下：
'''
2020-03-15 13:29:51 - MEGAHIT v1.2.9
2020-03-15 13:29:51 - Using megahit_core with POPCNT and BMI2 support
2020-03-15 13:29:51 - Convert reads to binary library
2020-03-15 13:29:55 - b'INFO sequence/io/sequence_lib.cpp : 77 - Lib 0 (/home/ubuntu/work/assembly/TARA_135_SRF_5-20_rep1_1m.khmer.pe.fq.gz): interleaved, 1958670 reads, 101 max length'
2020-03-15 13:29:58 - b'INFO sequence/io/sequence_lib.cpp : 77 - Lib 1 (/home/ubuntu/work/assembly/TARA_135_SRF_5-20_rep2_1m.khmer.pe.fq.gz): interleaved, 1965384 reads, 101 max length'
2020-03-15 13:29:58 - b'INFO utils/utils.h : 152 - Real: 7.6951\tuser: 2.6234\tsys: 0.4533\tmaxrss: 102644'
2020-03-15 13:29:58 - k-max reset to: 119
2020-03-15 13:29:58 - Start assembly. Number of CPU threads 4
2020-03-15 13:29:58 - k list: 21,29,39,59,79,99,119
2020-03-15 13:29:58 - Memory used: 8000000000
2020-03-15 13:29:58 - Extract solid (k+1)-mers for k = 21
2020-03-15 13:30:55 - Build graph for k = 21
......
2020-03-15 13:42:29 - Build graph for k = 119
2020-03-15 13:42:31 - Assemble contigs from SdBG for k = 119
2020-03-15 13:42:41 - Merging to output final contigs
2020-03-15 13:42:41 - 16707 contigs, total 8656583 bp, min 202 bp, max 9085 bp, avg 518 bp, N50 520 bp
2020-03-15 13:42:41 - ALL DONE. Time elapsed: 770.087650 seconds
#时间有点长，有十二分钟。可以休息下，或者继续阅读后面的内容，准备下。
real 12m50.137s
user 23m11.702s
sys 0m7.025s
'''
查看组装文件
输出文件会是TARA_135_SRF_khmer/TARA_135_SRF.contigs.fa。
#复制并重命名组装文件
cp ./TARA_135_SRF_khmer/*contigs.fa tara135_SRF_megahit.fasta
head tara135_SRF_megahit.fasta
我们为什么组装，组装的优点是什么，对于组装文件，我们能对它做些什么呢？
- 组装集减少读取冗余，因此组装每个转录本应具有大约一个序列，而不是读取，因为读取每个脚本具有许多读取。
- 组装的连续体比读取时间长，因此更容易对它们进行基因搜索。我们明天再报道！
- 程序集的错误也比读取少，因此样本比较等可能更准确。但是，如果数据覆盖范围确实很低，并且大量信息也会丢失，则程序集也可能消除某些数据。
进一步参考
还有其他的组装软件，你可以用您的数据尝试，其中一些列在我们的参考页。我们感兴趣的一个是PLASS，它在蛋白质水平组装！
组装评估和定量
Transrate是一个可用于几种不同类型的组装评估的程序。第一个也是最简单的方法是计算有关组装基于长度的指标，例如总碱基数和contigs的 N50。Transrate还可用于比较两个组装或给出一个分数，该分数表示为组装提供积极支持的输入reads的比例。有关指标以及如何运行基于参考的Transrate的更多信息，请参阅Smith-Unna 等人 2016 年的文档和论文。
#检查$PROJECT变量
echo $PROJECT
cd $PROJECT
#建立文件夹并进入
mkdir -p evaluation
cd evaluation
#把组装文件复制过来，应该是文件比较小就不链接了
cp $PROJECT/assembly/tara135_SRF_megahit.fasta ./
#使用transrate产生组装统计数据，这里我没有加环境变量，直接绝对路径运行了
./transrate-1.0.3-osx/transrate -h #查看帮助，测试软件可用
#统计
time ./transrate-1.0.3-osx/transrate --assembly tara135_SRF_megahit.fasta
'''
[ INFO] 2020-03-15 14:15:45 : Loading assembly: /Users/zd200572/work/evaluation/evaluation/tara135_SRF_megahit.fasta
[ INFO] 2020-03-15 14:15:46 : Analysing assembly: /Users/zd200572/work/evaluation/evaluation/tara135_SRF_megahit.fasta
[ INFO] 2020-03-15 14:15:46 : Results will be saved in /Users/zd200572/work/evaluation/evaluation/transrate_results/tara135_SRF_megahit
[ INFO] 2020-03-15 14:15:46 : Calculating contig metrics...
[ INFO] 2020-03-15 14:15:48 : Contig metrics:
[ INFO] 2020-03-15 14:15:48 : -----------------------------------
[ INFO] 2020-03-15 14:15:48 : n seqs 16707
[ INFO] 2020-03-15 14:15:48 : smallest 202
[ INFO] 2020-03-15 14:15:48 : largest 9085
[ INFO] 2020-03-15 14:15:48 : n bases 8656583
[ INFO] 2020-03-15 14:15:48 : mean len 518.14
[ INFO] 2020-03-15 14:15:48 : n under 200 0
[ INFO] 2020-03-15 14:15:48 : n over 1k 909
[ INFO] 2020-03-15 14:15:48 : n over 10k 0
[ INFO] 2020-03-15 14:15:48 : n with orf 5531
[ INFO] 2020-03-15 14:15:48 : mean orf percent 84.25
[ INFO] 2020-03-15 14:15:48 : n90 331
[ INFO] 2020-03-15 14:15:48 : n70 408
[ INFO] 2020-03-15 14:15:48 : n50 520
[ INFO] 2020-03-15 14:15:48 : n30 711
[ INFO] 2020-03-15 14:15:48 : n10 1208
[ INFO] 2020-03-15 14:15:48 : gc 0.5
[ INFO] 2020-03-15 14:15:48 : bases n 0
[ INFO] 2020-03-15 14:15:48 : proportion n 0.0
[ INFO] 2020-03-15 14:15:48 : Contig metrics done in 2 seconds
[ INFO] 2020-03-15 14:15:48 : No reads provided, skipping read diagnostics
[ INFO] 2020-03-15 14:15:48 : No reference provided, skipping comparative diagnostics
[ INFO] 2020-03-15 14:15:48 : Writing contig metrics for each contig to /Users/zd200572/work/evaluation/evaluation/transrate_results/tara135_SRF_megahit/contigs.csv
[ INFO] 2020-03-15 14:15:48 : Writing analysis results to assemblies.csv
real 0m4.218s
user 0m3.837s
sys 0m0.291s
'''
ln -s ../assembly/tara_f135_full_megahit.fasta
# full vs. subset 因为这里没有全部的，这步没做
./transrate-1.0.3-osx/transrate --reference=tara_f135_full_megahit.fasta --assembly=tara135_SRF_megahit.fasta --output=full_v_subset
# subset vs. full 因为这里没有全部的，这步没做
./transrate-1.0.3-osx/transrate --assembly=tara_f135_full_megahit.fasta --reference=tara135_SRF_megahit.fasta --output=subset_v_full
用salmon量化reads
这个组装对我们测序reads的代表性如何，我们将使用鲑鱼来量化表达。Salmon 是一种用于量化 RNAseq reads的新软件，它既运行快，又考虑了转录本的长度（Patro等人，2017 年）。
您可以在此处阅读更多有关salmon类似的"pseudoalignment"：
- 博客文章简介： http://robpatro.com/blog/?p=248
- 2016年博客文章评估和比较方法在这里
- salmonGithub
- 2018年论文：[A direct comparison of genome alignment and transcriptome pseudoalignment](https://www.biorxiv.org/content/early/2018/10/16/444620)
#首先链接文件
ln -s ${PROJECT}/trim/TARA_135_SRF_5-20_*.qc.fq.gz ./
#用salmon进行定量
#检查salmon是否可用，并查看运行选项：
salmon -h
'''
salmon v1.1.0
Usage: salmon -h|--help or
salmon -v|--version or
salmon -c|--cite or
salmon [--no-version-check] [-h | options]
Commands:
index Create a salmon index
quant Quantify a sample
alevin single cell analysis
swim Perform super-secret operation
quantmerge Merge multiple quantifications into a single file
' ''
#set -u 让你知道是否有任何未设置的变量，即如果变量未定义。
set -u
printf "\nMy trimmed data is in $PROJECT/trim/, and consists of $(ls -1 ${PROJECT}/trim/*.qc.fq.gz | wc -l) files\n\n"
set +u
cd ${PROJECT}
mkdir -p quant
cd quant
#文章中原文使用的是全部的数据集，因为手上没有，就用前面组装的那个代替了
ln -s $PROJECT/assembly/tara135_SRF_megahit.fasta ./tara_f135_full_megahit.fasta
#首先 索引
salmon index --index tara135 --transcripts tara_f135_full_megahit.fasta
'''
index ["tara135"] did not previously exist . . . creating it
[2020-03-15 15:18:00.055] [jLog] [info] building index
out : tara135
[2020-03-15 15:18:00.077] [puff::index::jointLog] [info] Running fixFasta
[Step 1 of 4] : counting k-mers
counted k-mers for 10000 transcripts
[2020-03-15 15:18:00.705] [puff::index::jointLog] [info] Replaced 0 non-ATCG nucleotides
[2020-03-15 15:18:00.705] [puff::index::jointLog] [info] Clipped poly-A tails from 22 transcripts
[2020-03-15 15:18:01.220] [puff::index::jointLog] [info] ntHll estimated 7962681 distinct k-mers, setting filter sizeto 2^28
Threads = 2
Vertex length = 31
Hash functions = 5
Filter size = 268435456
Capacity = 2
Files:
tara135/ref_k31_fixed.fa
--------------------------------------------------------------------------------
Round 0, 0:268435456
Pass Filling Filtering
1 3 87
2 76 0
True junctions count = 13582
False junctions count = 42694
Hash table size = 56276
Candidate marks count = 75419
--------------------------------------------------------------------------------
Reallocating bifurcations time: 0
True marks count: 61643
Edges construction time: 49
--------------------------------------------------------------------------------
Distinct junctions = 13582
approximateContigTotalLength: 7171187
counters:
245 39 48 2
contig count: 34564 element count: 9003711 complex nodes: 334
# of ones in rank vector: 34563
[2020-03-15 15:21:38.689] [puff::index::jointLog] [info] Setting the index/BinaryGfa directory tara135
size = 9003711
-----------------------------------------
| Loading contigs | Time = 2.9299 ms
-----------------------------------------
size = 9003711
-----------------------------------------
| Loading contig boundaries | Time = 2.6109 ms
-----------------------------------------
Number of ones: 34563
Number of ones per inventory item: 512
Inventory entries filled: 68
[2020-03-15 15:21:38.735] [puff::index::jointLog] [info] Done wrapping the rank vector with a rank9sel structure.
[2020-03-15 15:21:38.761] [puff::index::jointLog] [info] contig count for validation: 34563
[2020-03-15 15:21:38.792] [puff::index::jointLog] [info] Total # of Contigs : 34,563
[2020-03-15 15:21:38.792] [puff::index::jointLog] [info] Total # of numerical Contigs : 34,563
[2020-03-15 15:21:38.839] [puff::index::jointLog] [info]
Total # of segments we have position for : 34,563
[2020-03-15 15:21:38.845] [puff::index::jointLog] [info] total contig vec entries 45,794
[2020-03-15 15:21:38.845] [puff::index::jointLog] [info] bits per offset entry 16
[2020-03-15 15:21:38.896] [puff::index::jointLog] [info] there were 21,081 equivalence classes
[2020-03-15 15:21:38.952] [puff::index::jointLog] [info] # segments = 34,563
[2020-03-15 15:21:38.952] [puff::index::jointLog] [info] total length = 9,003,711
[2020-03-15 15:21:38.962] [puff::index::jointLog] [info] Reading the reference files ...
[2020-03-15 15:21:39.035] [puff::index::jointLog] [info] positional integer width = 24
[2020-03-15 15:21:39.035] [puff::index::jointLog] [info] seqSize = 9,003,711
[2020-03-15 15:21:39.035] [puff::index::jointLog] [info] rankSize = 9,003,711
[2020-03-15 15:21:39.035] [puff::index::jointLog] [info] edgeVecSize = 0
[2020-03-15 15:21:39.035] [puff::index::jointLog] [info] num keys = 7,966,821
for info, total work write each : 2.331 total work inram from level 3 : 4.322 total work raw : 25.000
[Building BooPHF] 100 % elapsed: 0 min 4 sec remaining: 0 min 0 sec
Bitarray 41749312 bits (100.00 %) (array + ranks )
final hash 0 bits (0.00 %) (nb in final hash 0)
[2020-03-15 15:21:43.505] [puff::index::jointLog] [info] mphf size = 4.97691 MB
[2020-03-15 15:21:43.514] [puff::index::jointLog] [info] chunk size = 4,501,856
[2020-03-15 15:21:43.516] [puff::index::jointLog] [info] chunk 0 = [0, 4,501,856)
[2020-03-15 15:21:43.525] [puff::index::jointLog] [info] chunk 1 = [4,501,856, 9,003,681)
[2020-03-15 15:21:44.832] [puff::index::jointLog] [info] finished populating pos vector
[2020-03-15 15:21:44.832] [puff::index::jointLog] [info] writing index components
[2020-03-15 15:21:51.498] [puff::index::jointLog] [info] finished writing dense pufferfish index
[2020-03-15 15:21:51.507] [jLog] [info] done building index
'''
#然后，比对定量
ln -s ../trim/*.qc.fq.gz ./
for sample in *1.qc.fq.gz
do
base=$(basename $sample _1.qc.fq.gz)
echo $base
salmon quant -i tara135 -p 2 -l A -1 ${base}_1.qc.fq.gz -2 ${base}_2.qc.fq.gz -o ${base}_quant
done
'''
将建立一系列文件夹，每个包含几个文件，
aux_info
cmd_info.json
lib_format_counts.json
libParams
logs
quant.sf
#以下是输出
TARA_135_DCM_5-20_rep1_1m
Version Info: Could not resolve upgrade information in the alotted time.
Check for upgrades manually at https://combine-lab.github.io/salmon
### salmon (mapping-based) v1.1.0
### [ program ] => salmon
### [ command ] => quant
### [ index ] => { tara135 }
### [ threads ] => { 2 }
### [ libType ] => { A }
### [ mates1 ] => { TARA_135_DCM_5-20_rep1_1m_1.qc.fq.gz }
### [ mates2 ] => { TARA_135_DCM_5-20_rep1_1m_2.qc.fq.gz }
### [ output ] => { TARA_135_DCM_5-20_rep1_1m_quant }
......
[2020-03-15 15:23:42.811] [jointLog] [info] Mapping rate = 7.51527%
[2020-03-15 15:23:42.811] [jointLog] [info] finished quantifyLibrary()
[2020-03-15 15:23:42.811] [jointLog] [info] Starting optimizer
[2020-03-15 15:23:42.838] [jointLog] [info] Marked 0 weighted equivalence classes as degenerate
[2020-03-15 15:23:42.839] [jointLog] [info] iteration = 0 | max rel diff. = 389.945
[2020-03-15 15:23:43.007] [jointLog] [info] iteration = 100 | max rel diff. = 4.15249e-16
[2020-03-15 15:23:43.010] [jointLog] [info] Finished optimizer
[2020-03-15 15:23:43.010] [jointLog] [info] writing output
'''
#查看下contig表达的相关信息
head -20 TARA_135_SRF_5-20_rep1_1m_quant/quant.sf
'''
Name Length EffectiveLength TPM NumReads
k119_0 520 307.784 37.218819 6.000
k119_1 365 183.149 31.273300 3.000
k119_8414 1160 975.533 27.399576 14.000
k119_2 585 380.586 45.148977 9.000
k119_3 429 245.318 38.913383 5.000
k119_8415 971 786.533 38.838355 16.000
k119_8416 391 208.092 64.224392 7.000
k119_4 1078 878.533 14266.484104 6564.736
k119_8417 434 250.228 30.519799 4.000
k119_8418 419 235.507 32.427598 4.000
k119_5 881 696.533 38.374625 14.000
k119_8419 308 130.702 58.429974 4.000
k119_6 341 160.515 59.471764 5.000
k119_8420 411 227.646 83.868114 10.000
k119_7 365 183.149 106.604899 10.226
k119_8421 818 633.533 75.340516 25.000
k119_8422 321 142.271 67.098405 5.000
k119_8423 371 188.900 70.749454 7.000
k119_8424 491 306.791 24.892887 4.000
'''
#查看下总比对率，因为没有真正的数据，所以比对率极低。。。。。。
less TARA_135_SRF_5-20_rep1_1m_quant/logs/salmon_quant.log
grep Mapping *quant/logs/*
'''
TARA_135_DCM_5-20_rep1_1m_quant/logs/salmon_quant.log:[2020-03-15 15:23:15.464] [jointLog] [info] Usage of --validateMappings implies use of minScoreFraction. Since not explicitly specified, it is being set to 0.65
...
TARA_137_DCM_5-20_rep2_1m_quant/logs/salmon_quant.log:[2020-03-15 15:27:18.742] [jointLog] [info] Mapping rate = 1.65539%
'''
评估读取映射的另一种方法
Transrate 实际上具有一种用于将reads"比对"到转录组的模式，并在读取映射上生成一些指标。read assessmentsalmon
./transrate --assembly=tara135_SRF_megahit.fasta --threads=2 --left=*_1.qc.fq.gz --right *_2.qc.fq.gz --output=${PROJECT}/evaluation/tara135_SRF_transrate
其他有用的教程和参考资料
- https://github.com/ngs-docs/2015-nov-adv-rna/blob/master/salmon.rst
- http://angus.readthedocs.io/en/2016/rob_quant/tut.html
- https://2016-aug-nonmodel-rnaseq.readthedocs.io/en/latest/quantification.html