Near-optimal RNA-Seq quantification https://pachterlab.github.io/kallisto

输入输出文件说明:http://bio.math.berkeley.edu/eXpress/manual.html

kallisto:Near-optimal RNA-Seq quantification-LMLPHP

文章标题:
 
Pseudoalignment for metagenomic read assignment
 
文章摘要:
 
We explore connections between metagenomic read assignment and the quantification of transcripts from RNA-Seq data. In particular, we show that the recent idea of pseudoalignment introduced in the RNA-Seq context is suitable in the metagenomics setting. When coupled with the Expectation-Maximization (EM) algorithm, reads can be assigned far more accurately and quickly than is currently possible with state of the art software.
 
源代码:
安装:
 
测试:
[[email protected] test]$ /project/metagenomics_benchmark/kallisto_linux-v0.43.0/kallisto index -i --index transcripts.fasta
 
[[email protected] test]$ /project/metagenomics_benchmark/kallisto_linux-v0.43.0/kallisto quant -i --index -o output reads_1.fastq reads_2.fastq(输入文件)
[[email protected] output]$ more abundance.tsv
target_id       length  eff_length      est_counts      tpm
NM_001168316    2283    2105.9  160.606 12581
NM_174914       2385    2207.9  1500.72 112128
NR_031764       1853    1675.9  102.671 10106.2
NM_004503       1681    1503.9  331.118 36320.7
NM_006897       1541    1363.9  664     80311.3
NM_014212       2037    1859.9  55      4878.25
NM_014620       2300    2122.9  591.166 45937.9
NM_017409       1959    1781.9  47      4351.17
NM_017410       2396    2218.9  42      3122.5
NM_018953       1612    1434.9  227.999 26212.1
NM_022658       2288    2110.9  4881    381446
NM_153633       1666    1488.9  361.044 40002.4
NM_153693       2072    1894.9  73.6719 6413.67
NM_173860       849     671.903 962     236189
NR_003084       1640    1462.9  0.00164208      0.18517
 
使用说明:

kallisto

kallisto是一个用高通量测序片段从RNA序列或更为普遍的目标序列中量化转录丰富度的一个程序。它是基于伪对齐的新的数据,用于快速确定reads目标,而无需alignment。在标准的RNA序列数据中,kallisto能够在mac系统上用不到十分钟的时间构建索引,用不到三分钟的时间量化(也就是分类)3千w人类的reads。reads伪对齐保留关键信息需要量化,并且kallisto不仅速度快,而且比现有的量化工具准确。事实上,由于伪对齐的过程是对reads出错上的健壮性,在许多基准中kallisto显著优于现有的工具。

kallisto能够用sleuth量化RNA序列分析。

kallisto产生的使用选项,这是一个列表:

kallisto 0.43.0

Usage: kallisto <CMD> [arguments] ..

Where <CMD> can be one of:

    index         Builds a kallisto index #构建一个kallisto索引
quant Runs the quantification algorithm #运行量化分析算法
pseudo Runs the pseudoalignment step#运行为比对
h5dump Converts HDF5-formatted results to plaintext#格式转换
version Prints version information#输出版本信息
cite Prints citation information#引用信息 Running kallisto <CMD> without arguments prints usage information for <CMD> 关于这些command说明如下: index :

kallisto index建立从靶序列的FASTA格式的文件的索引。该指数命令的参数有:

kallisto 0.43.0
Builds a kallisto index Usage: kallisto index [arguments] FASTA-files#输入文件 Required argument: #必选参数
-i, --index=STRING Filename for the kallisto index to be constructed #kallisto索引被构建的文件名 Optional argument:
-k, --kmer-size=INT k-mer (odd) length (default: 31, max value: 31)
--make-unique Replace repeated target names with unique names 输入文件为fasta格式,可以是压缩文件。

quant:

kallisto quant运行量化算法。对于定量命令的参数有:

kallisto 0.43.0
Computes equivalence classes for reads and quantifies abundances#对reads进行分类和物种丰富度评估 Usage: kallisto quant [arguments] FASTQ-files #输入文件 Required arguments: #必选参数
-i, --index=STRING Filename for the kallisto index to be used for
quantification #索引文件
-o, --output-dir=STRING Directory to write output to #输出文件目录 Optional arguments:
--bias Perform sequence based bias correction
-b, --bootstrap-samples=INT Number of bootstrap samples (default: 0)
--seed=INT Seed for the bootstrap sampling (default: 42)
--plaintext Output plaintext instead of HDF5
--single Quantify single-end reads
--fr-stranded Strand specific reads, first read forward
--rf-stranded Strand specific reads, first read reverse
-l, --fragment-length=DOUBLE Estimated average fragment length
-s, --sd=DOUBLE Estimated standard deviation of fragment length
(default: value is estimated from the input data)
-t, --threads=INT Number of threads to use (default: 1)
--pseudobam Output pseudoalignments in SAM format to stdout kallisto可以处理单端或双端的序列,默认情况下是双端序列,输入为fastq文件:
kallisto quant -i index -o output pairA_1.fastq pairA_2.fastq pairB_1.fastq pairB_2.fastq

对于单端序列可以用 选项 --single ,也可用用 -l 和 -s 选项,然后列出输入的fastq文件即可:

kallisto quant -i index -o output --single -l 200 -s 20 file1.fastq.gz file2.fastq.gz file3.fastq.gz

kallisto quant produces three output files by default:

kallisto定量分析默认产生三个输出文件:

  • abundances.h5 :二进制文件,包含运行信息,物种丰富度评估,bootstrap 评估等这个文件可以被sleuth打开阅读。
  • abundances.tsv :是一个物种丰富度的说明文件。
  • run_info.json  :是一个包含运行的相关信息
可选参数说明:
Pseudobam:
--pseudobam,所有的伪比对输出格式为格式。可以被定向到一个文件中,也可以用samtools转换成bam。
例如: kallisto quant -i index -o out --pseudobam r1.fastq r2.fastq > out.sam

或者用samtools:

kallisto quant -i index -o out --pseudobam r1.fastq r2.fastq | samtools view -Sb - > out.bam

kallisto:Near-optimal RNA-Seq quantification-LMLPHP

                  (学校的秋天,哈哈)

pseudo

kallisto pseudo只是在伪比对这一环节运行并且其目的是为在单细胞RNA的序列的使用。pseudo详细的命令选项如下:

kallisto 0.43.0
Computes equivalence classes for reads and quantifies abundances Usage: kallisto pseudo [arguments] FASTQ-files Required arguments:
-i, --index=STRING Filename for the kallisto index to be used for
pseudoalignment
-o, --output-dir=STRING Directory to write output to Optional arguments:
-u --umi First file in pair is a UMI file
-b --batch=FILE Process files listed in FILE
--single Quantify single-end reads
-l, --fragment-length=DOUBLE Estimated average fragment length
-s, --sd=DOUBLE Estimated standard deviation of fragment length
(default: value is estimated from the input data)
-t, --threads=INT Number of threads to use (default: 1)
--pseudobam Output pseudoalignments in SAM format to stdout

该命令的格式和参数的含义是与quant命令相同。然而,pseudo不运行EM算法来量化丰度。此外pseudo指令有一个选项在批处理文件中指定许多细胞,如:

kallisto pseudo -i index -o output -b batch.txt
 

h5dump

kallisto h5dump转换 hdf5格式。对于h5dump命令的参数有:

kallisto 0.43.0
Converts HDF5-formatted results to plaintext Usage: kallisto h5dump [arguments] abundance.h5 Required argument:
-o, --output-dir=STRING Directory to write output to

kallisto:Near-optimal RNA-Seq quantification-LMLPHP

05-11 16:26