Biostar workflows

Guide

在各个数据库中有大量的数据，可以通过一些工具进行下载：

parallel

当我们有一个文件如下所示：

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sample,group,file
control1,control,sample_J1_ABC
control2,control,sample_K2_DEF
control3,control,sample_L3_GHI
heatshock1,treatment,sample_Q1_JKL
heatshock2,treatment,sample_Q2_MNO
heatshock3,treatment,sample_Q3_PQR

我们想根据列明名去提取变量，并在parallel中运行，我们可以使用--header :以及--colseo ,然后就可以实现以下功能：
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cat design.csv | parallel --header : --colsep , \
              make -f snpcall.mk   \
              REF=refs/genome.fa   \
              R1=reads/{file}.fq   \  
              BAM=bam/{sample}.bam \
              run

访问meatadata

如果我们有NCBI accession number，我们可以通过bio searchmetadata：

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# Searches GenBank
bio search AF086833

# Searches SRA
bio search SRR1553425

# Searches NCBI Genomes
bio search GCA_000005845

# Also searches NCBI Genomes
bio search ecoli

下载GenBank数据

GenBank是NIH基因序列数据库，如果我们有一个Genebank的accession number，我们可以通过genbank.mk来下Genebank数据。例如我们想下载AF086833的FASTSA和GFF数据，我们可以通过以下命令：

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make -f src/run/genbank.mk fasta gff ACC=AF086833
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#
# Downloads NCBI data vie Entrez API
#

# Accession number at NCBI
ACC ?= AF086833

# The accession as a fasta file.
REF ?= refs/${ACC}.fa

# The accession as a genbank file.
GBK ?= refs/${ACC}.gb

# The accession as a gff file.
GFF ?= refs/${ACC}.gff

# Check the make version.
ifeq ($(origin .RECIPEPREFIX), undefined)
  $(error "### Error! Please use GNU Make 4.0 or later ###")
endif

# Makefile customizations.
.DELETE_ON_ERROR:
.ONESHELL:
SHELL := bash
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# genbank.mk: download sequences from GenBank"
	@echo "#"
	@echo "# ACC=${ACC}"
	@echo "# REF=${REF}"
	@echo "# GBK=${GBK}"
	@echo "# GFF=${GFF}"
	@echo "#"
	@echo "# make fasta genbank gff run"
	@echo "#"

# Obtain a sequence from NCBI.
${REF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format fasta > $@

${GBK}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} > $@

${GFF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format gff > $@

# Download FASTA file.
fasta::  ${REF}
	@ls -lh ${REF}

# Download GenBank file.
genbank::  ${GBK}
	@ls -lh ${GBK}

# Download GFF file.
gff::  ${GFF}
	@ls -lh ${GFF}

# Remove FASTA file.
fasta!::
	rm -rf ${REF}

# Remove GenBank file.
genbank!::
	rm -rf ${GBK}

# Remove GFF file.
gff!::
	rm -rf ${GFF}

# The run target is FASTA
run:: fasta

# Undo run target
run!:: fasta!

test:
	make -f src/run/genbank.mk fasta! fasta ACC=${ACC} REF=${REF}
	make -f src/run/genbank.mk gff! gff ACC=${ACC} GFF=${GFF}
	make -f src/run/genbank.mk genbank! genbank ACC=${ACC} GBK=${GBK}

# Installation instructions
install::
	@echo pip install bio --upgrade

# Targets that are not files.
.PHONY: usage install run run! test fasta fasta! genbank genbank! gff gff!

从NCBI访问参考基因组

NCBI在blog中提出从2024年6月，使用新的NCBI Datasets resource来访问参考基因组，从而替换旧的Genome和Assenbly resource。

但是，目前资源仍然有效，主要入口站点位于：

假如我们有一个人参考基因组的accession number，GCA_000001405.29我们可以先通过bio search查询：

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bio search GCA_000001405.29

它将输出：
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[
    {
        "assembly_accession": "GCA_000001405.29",
        "bioproject": "PRJNA31257",
        "biosample": "",
        "wgs_master": "",
        "refseq_category": "reference genome",
        "taxid": "9606",
        "species_taxid": "9606",
        "organism_name": "Homo sapiens",
        "infraspecific_name": "",
        "isolate": "",
        "version_status": "latest",
        "assembly_level": "Chromosome",
        "release_type": "Patch",
        "genome_rep": "Full",
        "seq_rel_date": "2022/02/03",
        "asm_name": "GRCh38.p14",
        "submitter": "Genome Reference Consortium",
        "gbrs_paired_asm": "GCF_000001405.40",
        "paired_asm_comp": "different",
        "ftp_path": "https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.29_GRCh38.p14",
        "excluded_from_refseq": "",
        "relation_to_type_materialasm_not_live_date": ""
    }
]

访问基因组所在的URL，然后即可通过前面的模块命令进行下载数据。

  • NCBI支持rsync协议（大多数网站不支持），可以使用rsync.mk进行下载数据，只需要将链接中的https改为rsync就可以下载完整目录来。
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    # You need to change the protocol to rsync in this case.
    URL=rsync://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/005/845/GCA_000005845.2_ASM584v2

    # You can use rsync directly
    rsync -avz $URL .

    # Or use our curl.mk makefile module.
    make -f src/run/rsync.mk get URL=$URL
rsync.mk 
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#
# Download data via rsync
#
# You might ask yourself Why have a makefile for what seems to be be trivial command?
#
# In this case to help us remember the correct commands and to make it fit with
# the rest of the workflow building process.
#
# The rsync rules everyone always forgets:
#
#   - trailing slash on URL means copy the contents of the directory
#   - no trailing slash on URL means copy the directory itself
#   - a slash on the destination directory has no effect
#

# The remote location we wish to download
URL ?= rsync://hgdownload.soe.ucsc.edu/goldenPath/eboVir3/bigZips

# The destination that the download will be placed in.
DEST ?= refs

# Makefile customizations.
SHELL := bash
.RECIPEPREFIX = >
.DELETE_ON_ERROR:
.ONESHELL:
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# General usage information.
usage::
> @echo ""
> @echo "# rsync.mk: downloads data via rsync protocol"
> @echo ""
> @echo "# make run URL=? FILE=?"
> @echo ""

# rsync will automatically detect updated files.
run::
> mkdir -p ${DEST}
> rsync --times --copy-links --recursive -vz -P ${URL} ${DEST}
> find ${DEST}/*

run!::
> @echo "# cannot undo an rsync download"

# Installation instructions
install::
> @echo "# no installation required"

从Ensembl访问数据

Ensembl是一个基因组数据库，了大量的数据，并且按照版本号进行归档，我们可以通过网页或者发布网站进行访问：

获取FASTQ数据

通过SRA下载

如果我们知道SRA编号，首先我们使用bio search搜索metadata:

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# Look up information on a SRR number.
bio search SRR1553425

我们会得到一些信息：
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[
    {
        "run_accession": "SRR1553425",
        "sample_accession": "SAMN02951957",
        "sample_alias": "EM110",
        "sample_description": "Zaire ebolavirus genome sequencing from 2014 outbreak in Sierra Leone",
        "first_public": "2015-06-05",
        "country": "Sierra Leone",
        "scientific_name": "Zaire ebolavirus",
        "fastq_bytes": "111859282;119350609",
        "base_count": "360534650",
        "read_count": "1784825",
        "library_name": "EM110_r1.ADXX",
        "library_strategy": "RNA-Seq",
        "library_source": "TRANSCRIPTOMIC",
        "library_layout": "PAIRED",
        "instrument_platform": "ILLUMINA",
        "instrument_model": "Illumina HiSeq 2500",
        "study_title": "Zaire ebolavirus Genome sequencing",
        "fastq_url": [
            "https://ftp.sra.ebi.ac.uk/vol1/fastq/SRR155/005/SRR1553425/SRR1553425_1.fastq.gz",
            "https://ftp.sra.ebi.ac.uk/vol1/fastq/SRR155/005/SRR1553425/SRR1553425_2.fastq.gz"
        ],
        "info": "112 MB, 119 MB file; 2 million reads; 360.5 million sequenced bases"
    }
]

上方的metadata中fastq_url就是我们的下载链接，我们也可以通过命令直接识别数据的URL:
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bio search SRR1553425 | jq -r '.[].fastq_url[]'`

对于大型数据，可以通过aria2去下载我们的数据，也可以使用自定义makesfile模块aria.mk：
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# The file to download.
URL=https://ftp.sra.ebi.ac.uk/vol1/fastq/SRR155/005/SRR1553425/SRR1553425_1.fastq.gz

# Download with 5 connections.
aria2c -x 5 -d reads $URL

或者

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URL=https://ftp.sra.ebi.ac.uk/vol1/fastq/SRR155/005/SRR1553425/SRR1553425_1.fastq.gz

# Use the aria module to download the file.
make -f src/run/aria.mk run URL=${URL} FILE=reads/SRR1553425_1.fastq.gz

我们也可以使用fastq-dump工具下载数据，使用自定义的fastq-dump.mk模块：

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make -f src/run/sra.mk run SRR=SRR1553425 N=ALL

如果我们有一个项目编号，也可以一次获取项目的所有数据：
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bio search PRJNA257197 --csv > project.csv

cat project.csv | csvcut -c 1 | \
    parallel  make -f src/run/sra.mk run SRR={} N=ALL

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#
# Download data via the aria2c downloader.
#

# The URL to be downloaded
URL ?= https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/bigZips/wuhCor1.fa.gz

# The directory to put the data into
DIR=.

# Destination file.
FILE ?= ${DIR}/$(notdir ${URL})

# Aria2c flags.
FLAGS = -x 5 -c --summary-interval=10

# Makefile customizations.
SHELL := bash
.DELETE_ON_ERROR:
.ONESHELL:
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# General usage information.
usage:
	@echo "#"
	@echo "# aria.mk: downloads data"
	@echo "#"
	@echo "#  make run URL=?"
	@echo "#"

# Download the file with aria2c.
${FILE}:
	mkdir -p $(dir $@)
	aria2c ${FLAGS} -o $@ ${URL}

run: ${FILE}
	@ls -lh $<

# Remove the file.
run!:
	rm -f ${FILE}

# A shortcut to default run
test: run

# Installation instructions
install:
	@echo "micromamba install aria2"

.PHONY:: usage run run! install test

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#
# Downloads sequencing reads from SRA.
#

# The directory that stores FASTQ reads that we operate on.
DIR ?= reads

# SRR number (sequencing run from the Ebola outbreak data of 2014)
SRR ?= SRR1553425

# The name of the unpacked reads
P1 ?= ${DIR}/${SRR}_1.fastq
P2 ?= ${DIR}/${SRR}_2.fastq

# The name of the final reads (may be the same)
R1 ?= ${P1}
R2 ?= ${P2}

# How many reads to download (N=ALL downloads everything).
N ?= ALL

# Makefile customizations.
.DELETE_ON_ERROR:
SHELL := bash
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# sra.mk: downloads FASTQ reads from SRA"
	@echo "#"
	@echo "# make run SRR=${SRR} N=${N}"
	@echo "#"

# Set the flags for the download
ifeq ($(N), ALL)
FLAGS ?= -F --split-files
else
FLAGS ?= -F --split-files -X ${N}
endif

# Obtain the reads from SRA.
${R1}:
	mkdir -p ${DIR}
	fastq-dump ${FLAGS} -O ${DIR} ${SRR}

	@if [ "${P1}" != "${R1}" ]; then \
		mv -f ${P1} ${R1}; \
	fi

	@if [ -f ${P2} ] && [ "${P2}" != "${R2}" ]; then \
		mv -f ${P2} ${R2}; \
	fi


# List the data. We don't know if the reads are paired or not.
run: ${R1}
	@if [ -f ${R2} ]; then
		@ls -lh ${R1} ${R2}
	else
		@ls -lh ${R1}
	fi

# Removes the SRA files.
run!:
	rm -f ${P1} ${P2} ${R1} ${R2}

test:
	# Get the FASTQ reads.
	make -f src/run/sra.mk SRR=${SRR} run! run

install::
	@echo micromamba install sra-tools

# Targets that are not files.
.PHONY: usage run run! test install

通过SRA Explorer

SRA Explorer工具旨在使序列读取档案中的数据集更易于访问。它具备 NCBI SRA 网站应有的一切功能。它是一个网页应用程序，可让您搜索数据集、查看元数据：

https://sra-explorer.info/

通过NCBI网站

可以直接访问SRA数据库，访问和浏览数据

https://www.ncbi.nlm.nih.gov/sra

通过URL下载数据

如果我们有一个数据的URL，我们可以通过wget``curl进行下载，这里也有一个curl.mk来实现这个功能：

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# The URL of the file.
URL=http://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.chromosome.22.fa.gz

# Download with curl
make -f src/run/curl.mk run URL=${URL} FILE=refs/chr22.fa.gz

对于大型数据，推荐使用aria软件，支持更快的多线程和断点下载。同样可以使用aria.mk来实现这个功能：

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# The URL of the file.
URL=http://ftp.ensembl.org/pub/release-104/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.chromosome.22.fa.gz

# Download with aria2
make -f src/run/aria.mk run URL=${URL} FILE=refs/chr22.fa.gz

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#
# Download data via the curl downloader.
#

# The URL to be downloaded
URL ?= https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/bigZips/wuhCor1.fa.gz

# Destination file.
FILE ?= refs/$(notdir ${URL})

# Aria2c flags.
FLAGS = -L

#
# Cute trick. The module also allows to fetch a limited number of lines
# from a remote gzipped url.
#

# How many lines to get
N =

# Apply the header
ifeq ($(strip $(N)),)
    HEAD =
else
    HEAD = | head -n ${N}
endif

# Should it unzip
GUNZIP  =

ifeq ($(strip $(GUNZIP)),)
    GUNZIP_CMD =
else
    GUNZIP_CMD  = | gunzip -c
endif

# Should it rezip
BGZIP =

ifeq ($(strip $(BGZIP)),)
    BGZIP_CMD =
else
    BGZIP_CMD  = | bgzip -@ 4 -c
endif

FILTER = ${GUNZIP_CMD} ${HEAD} ${BGZIP_CMD}

# Makefile customizations.
.DELETE_ON_ERROR:
.ONESHELL:
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# General usage information.
usage:
	@echo "#"
	@echo "# curl.mk: downloads data"
	@echo "#"
	@echo "#  make run URL=? FILE=?"
	@echo "#"

# Download the file with aria2c.
${FILE}:
	mkdir -p $(dir $@)
	curl ${FLAGS} ${URL} ${FILTER} > $@

run: ${FILE}
	@ls -lh $<

# For all cases remove the file and temporary file.
run!:
	rm -f ${FILE}

# Installation instructions
install:
	@echo "# no installation required"

.PHONY:: usage run run! install
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#
# Download data via the aria2c downloader.
#

# The URL to be downloaded
URL ?= https://hgdownload.soe.ucsc.edu/goldenPath/wuhCor1/bigZips/wuhCor1.fa.gz

# The directory to put the data into
DIR=.

# Destination file.
FILE ?= ${DIR}/$(notdir ${URL})

# Aria2c flags.
FLAGS = -x 5 -c --summary-interval=10

# Makefile customizations.
SHELL := bash
.DELETE_ON_ERROR:
.ONESHELL:
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# General usage information.
usage:
	@echo "#"
	@echo "# aria.mk: downloads data"
	@echo "#"
	@echo "#  make run URL=?"
	@echo "#"

# Download the file with aria2c.
${FILE}:
	mkdir -p $(dir $@)
	aria2c ${FLAGS} -o $@ ${URL}

run: ${FILE}
	@ls -lh $<

# Remove the file.
run!:
	rm -f ${FILE}

# A shortcut to default run
test: run

# Installation instructions
install:
	@echo "micromamba install aria2"

.PHONY:: usage run run! install test

refgenie工具

Refgenie 是一款命令行工具，可用于下载和管理参考基因组，也可以构建和管理自定义基因组集。

安装

可以使用coanda进行安装：

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coanda install refgenie

但是，一般来说，基于Python的命令行工具，我们可以使用pipx保存在单独的环境中，这样还有一个好处是refgenie可以所有的环境可用。
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pip install pipx
pipx ensurepath
pipx install refgenie

配置

创建配置文件

refgenie需要一个配置文件，每次运行时候都需要-c参数指定配置文件。更为方便的是，我们可以创建REFGENIEshell环境变量，来自动默认加载配置文件。
我们将我们的配置文件放在~/refs中（可以自定义），然后在~/.bashrc中添加：

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# Our configuration file is located in the ~/refs folder. 
export REFGENIE=~/refs/config.yaml

也可以直接命令行输入：
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echo "export REFGENIE=~/refs/config.yaml" >> ~/.bashrc

初始化配置文件

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refgenie init -c ~/refs/config.yaml

添加资源

refgenie可以订阅iGenomes服务器资源，托管额外的参考基因组数据。

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refgenie subscribe -s http://igenomes.databio.org/

检索

  • 列出远程基因组资源
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    refgenie listr

  • 列出本地基因组资源

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    refgenie list

  • 查找本地资源的路径：

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    refgenie seek hg38/gencode_gtf

使用

  • 下载资源

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    refgenie pull hg38/gencode_gtf

  • Bash脚本中使用

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    # Set the GTF variable to the path of the hg38/gencode_gtf resource
    REF=$(refgenie seek hg38/fasta)

  • Makefile中使用
    在makefile中，我们可以使用$(shell command)来执行命令，然后将结果赋值给变量，如下所示：

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    # Set the GTF variable to the path of the hg38/gencode_gtf resource
    GTF=$(shell refgenie seek hg38/gencode_gtf)

genomepy工具

genomepy是一个下载和使用基因组数据的工具，它可用于：

  • 搜索可用数据
  • 显示可用的metadata
  • 自动下载、预处理
  • 生成可选的比对索引

安装

同样有两种安装方式：

  • 使用micromamba安装

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    micromamba install genomepy

  • 或者使用pipx安装

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    pip install pipx
    pipx ensurepath
    pipx install genomepy

检索

搜索基因组：

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genomepy search ecoli > listing.txt

下载基因组：

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genomepy install ViralProj272395

默认的下载文件位置在~/.local/share/genomes/ViralProj272395中，可以通过-g参数指定下载位置。

下载注释文件

需要先安装一些软件包，然后进行检索下载。

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micromamba install ucsc-genepredtogtf ucsc-genepredtobed ucsc-bedtogenepred \
        ucsc-gtftogenepred ucsc-gff3togenepred

检索注释包

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genomepy annotation ViralProj272395

下载安装注释

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genomepy install ViralProj272395 --annotation

则目录~/.local/share/genomes/ViralProj272395包含以下内容：

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README.txt
ViralProj272395.annotation.bed
ViralProj272395.annotation.gtf
ViralProj272395.fa
ViralProj272395.fa.fai
ViralProj272395.fa.sizes
ViralProj272395.gaps.bed
assembly_report.txt

Recipes

短序列比对

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make -f src/recipes/short-read-alignments.mk run
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#
# Biostar Workflows: https://www.biostarhandbook.com/
#

# Genome accession.
ACC = KM233118

# SRR number.
SRR = SRR1972918

# Set both reads for paired end and only one for single end.
R1 = reads/SRR1972918_1.fq
R2 = reads/SRR1972918_2.fq

# The reference genome.
REF = refs/${ACC}.fa

# The name of the gff file.
GFF = refs/${ACC}.gff

# The alignment file.
BAM = bam/${SRR}.bam

# Makefile customizations.
SHELL := bash
.DELETE_ON_ERROR:
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

usage:
	@echo "#"
	@echo "# Align short reads to a reference genome"
	@echo "#"
	@echo "# make run"
	@echo "#"

# Download the reference genome.
genome:
	make -f src/run/genbank.mk fasta gff ACC=${ACC} REF=${REF} GFF=${GFF}

# Download the short reads.
fastq:
	make -f src/run/sra.mk run SRR=${SRR} N=2500 R1=${R1} R2=${R2}

# Perform data related tasks
data: genome fastq
	make -f src/run/bwa.mk index REF=${REF}

# Index and align the reads to the reference genome.
align:
	make -f src/run/bwa.mk run REF=${REF} R1=${R1} R2=${R2} BAM=${BAM}

# Run the complete pipeline.
run: data align

# Remove the alignment generated by the pipeline
run!:
	rm -f ${BAM}

.PHONY: usage data align run
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#
# Downloads NCBI data vie Entrez API
#

# Accession number at NCBI
ACC ?= AF086833

# The accession as a fasta file.
REF ?= refs/${ACC}.fa

# The accession as a genbank file.
GBK ?= refs/${ACC}.gb

# The accession as a gff file.
GFF ?= refs/${ACC}.gff

# Check the make version.
ifeq ($(origin .RECIPEPREFIX), undefined)
  $(error "### Error! Please use GNU Make 4.0 or later ###")
endif

# Makefile customizations.
.DELETE_ON_ERROR:
.ONESHELL:
SHELL := bash
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# genbank.mk: download sequences from GenBank"
	@echo "#"
	@echo "# ACC=${ACC}"
	@echo "# REF=${REF}"
	@echo "# GBK=${GBK}"
	@echo "# GFF=${GFF}"
	@echo "#"
	@echo "# make fasta genbank gff run"
	@echo "#"

# Obtain a sequence from NCBI.
${REF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format fasta > $@

${GBK}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} > $@

${GFF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format gff > $@

# Download FASTA file.
fasta::  ${REF}
	@ls -lh ${REF}

# Download GenBank file.
genbank::  ${GBK}
	@ls -lh ${GBK}

# Download GFF file.
gff::  ${GFF}
	@ls -lh ${GFF}

# Remove FASTA file.
fasta!::
	rm -rf ${REF}

# Remove GenBank file.
genbank!::
	rm -rf ${GBK}

# Remove GFF file.
gff!::
	rm -rf ${GFF}

# The run target is FASTA
run:: fasta

# Undo run target
run!:: fasta!

test:
	make -f src/run/genbank.mk fasta! fasta ACC=${ACC} REF=${REF}
	make -f src/run/genbank.mk gff! gff ACC=${ACC} GFF=${GFF}
	make -f src/run/genbank.mk genbank! genbank ACC=${ACC} GBK=${GBK}

# Installation instructions
install::
	@echo pip install bio --upgrade

# Targets that are not files.
.PHONY: usage install run run! test fasta fasta! genbank genbank! gff gff!
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#
# Downloads sequencing reads from SRA.
#

# The directory that stores FASTQ reads that we operate on.
DIR ?= reads

# SRR number (sequencing run from the Ebola outbreak data of 2014)
SRR ?= SRR1553425

# The name of the unpacked reads
P1 ?= ${DIR}/${SRR}_1.fastq
P2 ?= ${DIR}/${SRR}_2.fastq

# The name of the final reads (may be the same)
R1 ?= ${P1}
R2 ?= ${P2}

# How many reads to download (N=ALL downloads everything).
N ?= 10000

# Makefile customizations.
.DELETE_ON_ERROR:
SHELL := bash
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# sra.mk: downloads FASTQ reads from SRA"
	@echo "#"
	@echo "# make run SRR=${SRR} N=${N}"
	@echo "#"

# Set the flags for the download
ifeq ($(N), ALL)
FLAGS ?= -F --split-files
else
FLAGS ?= -F --split-files -X ${N}
endif

# Obtain the reads from SRA.
${R1}:
	mkdir -p ${DIR}
	fastq-dump --gzip ${FLAGS} -O ${DIR} ${SRR}

	@if [ "${P1}" != "${R1}" ]; then \
		mv -f ${P1} ${R1}; \
	fi

	@if [ -f ${P2} ] && [ "${P2}" != "${R2}" ]; then \
		mv -f ${P2} ${R2}; \
	fi


# List the data. We don't know if the reads are paired or not.
run: ${R1}
	@if [ -f ${R2} ]; then
		@ls -lh ${R1} ${R2}
	else
		@ls -lh ${R1}
	fi

# Removes the SRA files.
run!:
	rm -f ${P1} ${P2} ${R1} ${R2}

test:
	# Get the FASTQ reads.
	make -f src/run/sra.mk SRR=${SRR} run! run

install::
	@echo micromamba install sra-tools

# Targets that are not files.
.PHONY: usage run run! test install
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#
# Generate alignments with bwa
#

# The reference genome.
REF ?= refs/genome.fa

# The directory that holds the index.
IDX_DIR = $(dir ${REF})/idx

# The name of the index
IDX ?= ${IDX_DIR}/$(notdir ${REF})

# A file in the index directory.
IDX_FILE ?= ${IDX}.ann

# Number of CPUS
NCPU ?= 2

# Additional flags to pass to BWA.
BWA_FLAGS ?= -t ${NCPU}

# Sam filter flags to filter the BAM file before sorting.
SAM_FLAGS ?=

# First in pair.
R1 ?= reads/reads1.fq

# Second in pair. Runs in single end mode if not defined.
R2 ?=

# The alignment file.
BAM ?= bam/alignment.bam

# Unsorted BAM file.
BAM_TMP ?= $(basename ${BAM}).unsorted.bam

# Set the values for the read groups.
ID ?= run1
SM ?= sample1
LB ?= library1
PL ?= ILLUMINA

# Build the read groups tag.
RG ?= '@RG\tID:${ID}\tSM:${SM}\tLB:${LB}\tPL:${PL}'

# Makefile customizations.
.DELETE_ON_ERROR:
SHELL := bash
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage:
	@echo "#"
	@echo "# bwa.mk: align reads using BWA"
	@echo "#"
	@echo "# R1=${R1}"
ifneq (${R2},)
	@echo "# R2=${R2}"
endif
	@echo "# REF=${REF}"
	@echo "# IDX=${IDX}"
	@echo "# BAM=${BAM}"
	@echo "#"
	@echo "# make index align"
	@echo "#"

# Read 1 must exist.
${R1}:
	@echo "# Read 1 file not found: R1=${R1}"
	@exit -1

# If R2 is set, it must exist.
ifneq (${R2},)
${R2}:
	@echo "# Read 2 file not found: R2=${R2}"
	@exit -1
endif

# Bail out if reference is not found
${REF}:
	echo "# Reference not found: ${REF}";
	exit -1

# Index the reference genome.
${IDX_FILE}: ${REF}
	@mkdir -p $(dir $@)
	bwa index -p ${IDX} ${REF}

# Create the index.
index: ${IDX_FILE}
	@echo "# bwa index: ${IDX}"

# Remove the index.
index!:
	rm -rf ${IDX_FILE}

# Paired end alignment.
${BAM_TMP}: ${R1} ${R2}
	@if [ ! -f ${IDX_FILE} ]; then
		echo "# bwa index not found: IDX=${IDX}";
		exit -1
	fi
	mkdir -p $(dir $@)
	bwa mem ${BWA_FLAGS} -R ${RG} ${IDX} ${R1} ${R2} | samtools view -b ${SAM_FLAGS} -o ${BAM_TMP}

# Sort the BAM file.
${BAM}: ${BAM_TMP}
	mkdir -p $(dir $@)
	samtools sort -@ ${NCPU} ${BAM_TMP} -o ${BAM}

# Create the BAM index file.
${BAM}.bai: ${BAM}
	samtools index ${BAM}

# Generate the alignment.
align: ${BAM}.bai
	@ls -lh ${BAM}

# Generate the alignment.
align!:
	rm -f ${BAM_TMP} ${BAM} ${BAM}.bai

# Create a wiggle coverage file.
wiggle: ${BAM}.bai ${REF}
	@make -f src/run/wiggle.mk BAM=${BAM} REF=${REF} run

# Running creates the wiggle file as well.
run: align wiggle

# Remove the BAM file.
run!:
	rm -rf ${BAM} ${BAM_TMP} ${BAM}.bai

# Run the test.
test:
	make -f src/run/tester.mk test_aligner MOD=src/run/bwa.mk

# The name of the stats file.
STATS = $(basename ${BAM}).stats

# Generate stats on the alignme
${STATS}: ${BAM}.bai
	samtools flagstat ${BAM} > ${STATS}

# Trigger the statistics generation.
stats: ${STATS}
	@echo "# ${STATS}"
	@cat ${STATS}

# Install required software.
install::
	@echo micromamba install bwa samtools



# Targets that are not files.
.PHONY: usage index align run run! test wiggle install

检测基因组变异

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make -f src/recipes/variant-calling.mk run
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#
# Biostar Workflows: https://www.biostarhandbook.com/
#

# Genome accession.
ACC = KM233118

# The reference genome.
REF = refs/${ACC}.fa

# SRR number.
SRR = SRR1972918

# The read pairs.
R1 = reads/${SRR}.fq
R2 = reads/${SRR}.fq

# The alignment file.
BAM = bam/${SRR}.bam

# The variants with bcftools.
VCF = vcf/${SRR}.vcf.gz

# Number of CPUS
NCPU = 4

# Makefile customizations.
SHELL := bash
.DELETE_ON_ERROR:
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

usage:
	@echo "#"
	@echo "# Short read variant calling."
	@echo "#"
	@echo "#"
	@echo "# make run"
	@echo "#"

run:
	# Download the reference genome.
	make -f src/run/genbank.mk fasta gff ACC=${ACC} REF=${REF}

	# Download the short reads.
	make -f src/run/sra.mk run SRR=${SRR} N=2500 R1=${R1} R2=${R2}

	# Index the genome then align the reads to the reference genome.
	make -f src/run/bwa.mk index run R1=${R1} R2=${R2} REF=${REF} BAM=${BAM} NCPU=${NCPU}

	# Call variants with bcftools.
	make -f src/run/bcftools.mk run REF=${REF} BAM=${BAM} VCF=${VCF} NCPU=${NCPU}

# Remove the variant calls generated by the pipeline
run!:
	rm -f ${VCF}
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#
# Downloads NCBI data vie Entrez API
#

# Accession number at NCBI
ACC ?= AF086833

# The accession as a fasta file.
REF ?= refs/${ACC}.fa

# The accession as a genbank file.
GBK ?= refs/${ACC}.gb

# The accession as a gff file.
GFF ?= refs/${ACC}.gff

# Check the make version.
ifeq ($(origin .RECIPEPREFIX), undefined)
  $(error "### Error! Please use GNU Make 4.0 or later ###")
endif

# Makefile customizations.
.DELETE_ON_ERROR:
.ONESHELL:
SHELL := bash
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# genbank.mk: download sequences from GenBank"
	@echo "#"
	@echo "# ACC=${ACC}"
	@echo "# REF=${REF}"
	@echo "# GBK=${GBK}"
	@echo "# GFF=${GFF}"
	@echo "#"
	@echo "# make fasta genbank gff run"
	@echo "#"

# Obtain a sequence from NCBI.
${REF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format fasta > $@

${GBK}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} > $@

${GFF}:
	mkdir -p $(dir $@)
	bio fetch ${ACC} --format gff > $@

# Download FASTA file.
fasta::  ${REF}
	@ls -lh ${REF}

# Download GenBank file.
genbank::  ${GBK}
	@ls -lh ${GBK}

# Download GFF file.
gff::  ${GFF}
	@ls -lh ${GFF}

# Remove FASTA file.
fasta!::
	rm -rf ${REF}

# Remove GenBank file.
genbank!::
	rm -rf ${GBK}

# Remove GFF file.
gff!::
	rm -rf ${GFF}

# The run target is FASTA
run:: fasta

# Undo run target
run!:: fasta!

test:
	make -f src/run/genbank.mk fasta! fasta ACC=${ACC} REF=${REF}
	make -f src/run/genbank.mk gff! gff ACC=${ACC} GFF=${GFF}
	make -f src/run/genbank.mk genbank! genbank ACC=${ACC} GBK=${GBK}

# Installation instructions
install::
	@echo pip install bio --upgrade

# Targets that are not files.
.PHONY: usage install run run! test fasta fasta! genbank genbank! gff gff!
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#
# Downloads sequencing reads from SRA.
#

# The directory that stores FASTQ reads that we operate on.
DIR ?= reads

# SRR number (sequencing run from the Ebola outbreak data of 2014)
SRR ?= SRR1553425

# The name of the unpacked reads
P1 ?= ${DIR}/${SRR}_1.fastq
P2 ?= ${DIR}/${SRR}_2.fastq

# The name of the final reads (may be the same)
R1 ?= ${P1}
R2 ?= ${P2}

# How many reads to download (N=ALL downloads everything).
N ?= 10000

# Makefile customizations.
.DELETE_ON_ERROR:
SHELL := bash
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage::
	@echo "#"
	@echo "# sra.mk: downloads FASTQ reads from SRA"
	@echo "#"
	@echo "# make run SRR=${SRR} N=${N}"
	@echo "#"

# Set the flags for the download
ifeq ($(N), ALL)
FLAGS ?= -F --split-files
else
FLAGS ?= -F --split-files -X ${N}
endif

# Obtain the reads from SRA.
${R1}:
	mkdir -p ${DIR}
	fastq-dump ${FLAGS} -O ${DIR} ${SRR}

	@if [ "${P1}" != "${R1}" ]; then \
		mv -f ${P1} ${R1}; \
	fi

	@if [ -f ${P2} ] && [ "${P2}" != "${R2}" ]; then \
		mv -f ${P2} ${R2}; \
	fi


# List the data. We don't know if the reads are paired or not.
run: ${R1}
	@if [ -f ${R2} ]; then
		@ls -lh ${R1} ${R2}
	else
		@ls -lh ${R1}
	fi

# Removes the SRA files.
run!:
	rm -f ${P1} ${P2} ${R1} ${R2}

test:
	# Get the FASTQ reads.
	make -f src/run/sra.mk SRR=${SRR} run! run

install::
	@echo micromamba install sra-tools

# Targets that are not files.
.PHONY: usage run run! test install
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#
# Generate alignments with bwa
#

# The reference genome.
REF ?= refs/genome.fa

# The directory that holds the index.
IDX_DIR = $(dir ${REF})/idx

# The name of the index
IDX ?= ${IDX_DIR}/$(notdir ${REF})

# A file in the index directory.
IDX_FILE ?= ${IDX}.ann

# Number of CPUS
NCPU ?= 2

# Additional flags to pass to BWA.
BWA_FLAGS ?= -t ${NCPU}

# Sam filter flags to filter the BAM file before sorting.
SAM_FLAGS ?=

# First in pair.
R1 ?= reads/reads1.fq

# Second in pair. Runs in single end mode if not defined.
R2 ?=

# The alignment file.
BAM ?= bam/alignment.bam

# Unsorted BAM file.
BAM_TMP ?= $(basename ${BAM}).unsorted.bam

# Set the values for the read groups.
ID ?= run1
SM ?= sample1
LB ?= library1
PL ?= ILLUMINA

# Build the read groups tag.
RG ?= '@RG\tID:${ID}\tSM:${SM}\tLB:${LB}\tPL:${PL}'

# Makefile customizations.
.DELETE_ON_ERROR:
SHELL := bash
.ONESHELL:
.SHELLFLAGS := -eu -o pipefail -c
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# Print usage information.
usage:
	@echo "#"
	@echo "# bwa.mk: align reads using BWA"
	@echo "#"
	@echo "# R1=${R1}"
ifneq (${R2},)
	@echo "# R2=${R2}"
endif
	@echo "# REF=${REF}"
	@echo "# IDX=${IDX}"
	@echo "# BAM=${BAM}"
	@echo "#"
	@echo "# make index align"
	@echo "#"

# Read 1 must exist.
${R1}:
	@echo "# Read 1 file not found: R1=${R1}"
	@exit -1

# If R2 is set, it must exist.
ifneq (${R2},)
${R2}:
	@echo "# Read 2 file not found: R2=${R2}"
	@exit -1
endif

# Bail out if reference is not found
${REF}:
	echo "# Reference not found: ${REF}";
	exit -1

# Index the reference genome.
${IDX_FILE}: ${REF}
	@mkdir -p $(dir $@)
	bwa index -p ${IDX} ${REF}

# Create the index.
index: ${IDX_FILE}
	@echo "# bwa index: ${IDX}"

# Remove the index.
index!:
	rm -rf ${IDX_FILE}

# Paired end alignment.
${BAM_TMP}: ${R1} ${R2}
	@if [ ! -f ${IDX_FILE} ]; then
		echo "# bwa index not found: IDX=${IDX}";
		exit -1
	fi
	mkdir -p $(dir $@)
	bwa mem ${BWA_FLAGS} -R ${RG} ${IDX} ${R1} ${R2} | samtools view -b ${SAM_FLAGS} -o ${BAM_TMP}

# Sort the BAM file.
${BAM}: ${BAM_TMP}
	mkdir -p $(dir $@)
	samtools sort -@ ${NCPU} ${BAM_TMP} -o ${BAM}

# Create the BAM index file.
${BAM}.bai: ${BAM}
	samtools index ${BAM}

# Generate the alignment.
align: ${BAM}.bai
	@ls -lh ${BAM}

# Generate the alignment.
align!:
	rm -f ${BAM_TMP} ${BAM} ${BAM}.bai

# Create a wiggle coverage file.
wiggle: ${BAM}.bai ${REF}
	@make -f src/run/wiggle.mk BAM=${BAM} REF=${REF} run

# Running creates the wiggle file as well.
run: align wiggle

# Remove the BAM file.
run!:
	rm -rf ${BAM} ${BAM_TMP} ${BAM}.bai

# Run the test.
test:
	make -f src/run/tester.mk test_aligner MOD=src/run/bwa.mk

# The name of the stats file.
STATS = $(basename ${BAM}).stats

# Generate stats on the alignme
${STATS}: ${BAM}.bai
	samtools flagstat ${BAM} > ${STATS}

# Trigger the statistics generation.
stats: ${STATS}
	@echo "# ${STATS}"
	@cat ${STATS}

# Install required software.
install::
	@echo micromamba install bwa samtools



# Targets that are not files.
.PHONY: usage index align run run! test wiggle install

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#
# Generates SNP calls with bcftools.
#

# A root to derive output default names from.
SRR = SRR1553425

# Number of CPUS
NCPU ?= 2

# Genbank accession number.
ACC ?= AF086833

# The reference genome.
REF ?= refs/${ACC}.fa

# The alignment file.
BAM ?= bam/${SRR}.bam

# The variant file.
VCF ?= vcf/$(notdir $(basename ${BAM})).vcf.gz

# Additional bcf flags for pileup annotation.
PILE_FLAGS =  -d 100 --annotate 'INFO/AD,FORMAT/DP,FORMAT/AD,FORMAT/ADF,FORMAT/ADR,FORMAT/SP'

# Additional bcf flags for calling process.
CALL_FLAGS = --ploidy 2 --annotate 'FORMAT/GQ'

# Makefile customizations.
SHELL := bash
.DELETE_ON_ERROR:
.ONESHELL:
MAKEFLAGS += --warn-undefined-variables --no-print-directory

# The first target is always the help.
usage::
	@echo "#"
	@echo "# bcftools.mk: calls variants using bcftools"
	@echo "#"
	@echo "# REF=${REF}"
	@echo "# BAM=${BAM}"
	@echo "# VCF=${VCF}"
	@echo "#"
	@echo "# make run"
	@echo "#"

${VCF}: ${BAM} ${REF}
	mkdir -p $(dir $@)
	bcftools mpileup ${PILE_FLAGS} -O u -f ${REF} ${BAM} | \
		bcftools call ${CALL_FLAGS} -mv -O u | \
		bcftools norm -f ${REF} -d all -O u | \
		bcftools sort -O z > ${VCF}

${VCF}.tbi: ${VCF}
	bcftools index -t -f $<

run:: ${VCF}.tbi
	@ls -lh ${VCF}

run!::
	rm -rf ${VCF} ${VCF}.tbi

install::
	@echo micromamba install bcftools

# Test the entire pipeline.
test:
	make -f src/run/tester.mk test_caller MOD=src/run/bwa.mk CALL=src/run/bcftools.mk

# Targets that are not files.
.PHONY: run run! install usage index test

bcftools

freebayes

GATK

DeepVariant