User talk:Darek Kedra/sandbox 28: Difference between revisions
Darek Kedra (talk | contribs) |
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* sequences from 70bp up to 1Mbp: | * sequences from 70bp up to 1Mbp: | ||
There are two algorithms for these: BWA-SW (Smith Waterman) and BWA-MEM(seeding alignments with maximal exact matches (MEMs) and then extending seeds with the affine-gap Smith-Waterman algorithm (SW)) | There are two algorithms for these: BWA-SW (Smith Waterman) and BWA-MEM(seeding alignments with maximal exact matches (MEMs) and then extending seeds with the affine-gap Smith-Waterman algorithm (SW)) | ||
Please note that BWA-SW requires different algorithm for indexing the genome. The default indexing algorithm is called IS. | |||
<pre> | <pre> | ||
#creating genome index | #creating genome index | ||
Line 745: | Line 747: | ||
#mapping single end reads using MEM algorithm | #mapping single end reads using MEM algorithm | ||
bwa mem ref.fa reads.fq > short_reads.bwa_mem.sam | bwa mem -p ref.bwa_is ref.fa reads.fq > short_reads.bwa_mem.sam | ||
#mapping paired end reads using MEM algorithm | #mapping paired end reads using MEM algorithm | ||
bwa mem ref. | bwa mem ref.bwa_is reads_1.fq reads_2.fq > reads_12.bwa_mem.sam | ||
#mapping single and reads | #mapping single and reads | ||
bwa aln ref. | bwa aln ref.bwa_is short_read.fq > short_read.bwa_aln.sai | ||
bwa samse ref. | bwa samse ref.bwa_is short_read.bwa_aln.sai short_read.fq > short_read.bwa_aln.sam | ||
#mapping paired reads | #mapping paired reads | ||
bwa aln ref. | bwa aln ref.bwa_is short_read_1.fq > short_read_1.bwa_aln.sai | ||
bwa aln ref. | bwa aln ref.bwa_is short_read_2.fq > short_read_2.bwa_aln.sai | ||
bwa sampe ref. | bwa sampe ref.bwa_is short_read_1.bwa_aln.sai short_read_2.bwa_aln.sai short_read_1.fq short_read_2.fq > short_read_12.bwa_aln.sam | ||
#mapping long reads using bwasw algorithm | #mapping long reads using bwasw algorithm | ||
bwa index -p ref.bwa_sw -a bwtsw ref.fa | |||
bwa bwasw ref. | bwa bwasw ref.bwa_sw long_read.fq > long_read.bwa_sw.sam | ||
</pre> | </pre> | ||
The mode currently recommended for mapping by BWA manual and the leading SNP calling software called GATK is MEM. | |||
===last=== | ===last=== |
Revision as of 07:30, 13 November 2013
Winterschool program
Introduction to Linux and the command line
- why Linux?
- runs on everything from cell phones to supercomputers
- long history of stable tools
- most of the bioinformatics software was written and intended to run on Linux
logging in, connecting to other servers with ssh / sftp
As with other computers, one requires username password combination to connect to a specific computer. This combination can be specific to each of the computers or shared between i.e. all workstations at a given location.
SSH is a name for secure, encrypted connections between computers. It consist of two components, ssh server running on a remote machine and a ssh client on your laptop / workstation. The client is included in the default installations of recent Linux and OS X (Mac), but on Windows one has to install it ( http://www.chiark.greenend.org.uk/~sgtatham/putty/download.html ). It is not required you have it for the course, but it is a good idea to have it on your computer if you want to access remote servers on the command line.
With properly configured ssh connection (on Linux and Mac) you can run not just command line programs but also graphical user interface. This is also possible on Windows, but it is way more complicated to set up.
XXX Emilio: start vagrant or Linux from inside your virtual box machine, log in XXX
file and directories naming
Linux is case sensitive, do MyFile.txt is different from MYFILE.TXT or myfile.txt. Try to use some consistent naming schemes for your project directories, input data or result files. You can use very long, descriptive names, like these result files from ENCODE project:
wgEncodeUwTfbsNhdfneoCtcfStdAlnRep0.bam_VS_wgEncodeUwTfbsNhdfneoInputStdAlnRep1.bam.regionPeak.gz
Things to keep in mind:
- never ever use space/tabs in your file/directory names
- avoid Unix special characters in file names (!?"'%&^~*$|/\{}[]()<>:)
As with Windows and Mac systems one can imagine the systems of directories (synonym of folders) as a giant tree, where each of the branches has an upper directory and may contain lower level directories (sub-directories). At the top of this branched hierarchy it is just one place from where everything starts (no C:, D: X: drives), and in Unix speak it is called root directory. Some examples of directory naming:
/home/linus/ /usr/local/bin /home/linus/bioinf/programs /home/linus/projects/chicken
Individual users have separate directories in which they store their data, results etc.
Some shortcuts to remember (what is after # sign is a description):
/ #root directory ~ #your home directory .. #one directory above . #current directory
There are few commands to move between directories, create new ones, and list what is located there:
pwd #print working directory = show where you are ls #list what is located in the current directory ls -l #list what is located in the current directory with details cd /home/linus/projects/chicken #go to this directory cd - # special shortcut to go back to the directory you have been before cd / #go to the root directory cd ~ #go to your home directory spec cd .. #go todirectory one level above the current one mkdir mynew_directory #create new directory rmdir myold_directory #remove some directory (it must be empty!)
absolute vs relative directory naming
We can do all the operations on directories or files using two conventions:
# absolute directory path starting with root "/" ls /home/linus/projects/chicken/genome.fasta #relative directory path (lets assume we are in /home/linus/projects/banana/) ls ../chicken/genome.fasta
This is useful shortcut for saving typing and avoiding errors when i.e. working on different systems.
permissions
In order to restrict actions associated with a given file or directory, each entity has 3 flags (r = read, w = write, e = execute) for 3 groups of users (file owner, group to which file owner belongs, i.e. students, and "all" for all the remaining users on that computer). So we have 9 fields describing what each of these groups can do with the file. On the top of it (or rather in the front of the string) we have another flag to tell us about what kind of thing it is ("-" = just a regular file, d = directory, l = link, etc.)
By default, owner has read+write permissions, his group members can read but not write (modify or delete) his files, and the rest of the word should have no right to see content of his files. These permissions are visible when listing content of a directory with "ls -l":
-rwxr-xr-x 1 vagrant users 49 Nov 7 14:43 my_program.py -rw-r----- 1 vagrant users 1812 Nov 7 14:39 myfile01.txt ---------- 1 vagrant users 9045 Nov 7 14:41 myfile02.txt -r--r--r-- 1 vagrant users 2016 Nov 7 14:39 myfile03.txt -rw------- 1 vagrant users 67863 Nov 7 14:40 myfile04.txt -rw-rw-rw- 1 vagrant users 8125 Nov 7 14:40 myfile05.txt -rw-rw---- 1 vagrant users 7233 Nov 7 14:40 myfile06.txt lrwxrwxrwx 1 vagrant users 12 Nov 7 14:50 myfile07.txt -> myfile06.txt drwxr-xr-x 2 vagrant users 0 Nov 7 14:50 test_dir1
To change permissions, we have to specify first the group we want to modify, action (add permissions or remove them) then the permission themselves, finaly the name of the file(s):
chmod a+x my_new_script.py #add execute permission to for all chmod a-w # remove write permission for owner, group and the rest
This is often useful for sharing data / results with other users on the same machine or when writing scripts/executing some programs downloaded from the net.
copy, rename/move files, create symbolic links
Often we need to organize files in directories from the existing ones, which requires moving thing around, making copies, renaming directories and files. To save space or to avoid using long path names, we can create links to other existing files or folders.
CAVEAT: both cp and mv commands will perform the task without warning if the destination file exist. It is one of the ways of destroying your data. To avoid this error use "cp -i" or "mv -i" which will ask you for confirmation when you are overwriting existing file.
#copy move rename pattern: cp/mv source destination cp file1.txt file2.txt cp file1.txt /some/directory/of/your/choice/ cp /home/linus/projecs/linus/file1.txt . #notice the space and the dot "." at the end of the command. The command is copy the file to the current directory
Instead of doing copy (original file stays intact) one can do mv (move/rename) where only the new copy remains and the old one is destroyed.
mv file1.txt file2.txt mv file1.txt /some/directory/of/your/choice/ mv /home/linus/projecs/linus/file1.txt . #notice the space and the dot "." at the end of the command. The command is copy the file to the current directory
Linking is a way of organizing your data. I.e. you have one big genome fasta file, and want to map your next generation sequencing reads using different mappers, which will require using this one file as an input to several programs. You can store your fasta file in one directory (i.e. /home/vagrant/projects/chicken/genome1.fa) and create links to this file in several subdirectories /home/vagrant/projects/chicken/bwa_mapping/, /home/vagrant/projects/chicken/bowtie2_mapping/, etc.). The command:
cd /home/vagrant/projects/chicken/bwa_mapping/ ln -s /home/vagrant/projects/chicken/genome1.fa . cd /home/vagrant/projects/chicken/bowtie2_mapping/ ln -s /home/vagrant/projects/chicken/genome1.fa .
view files (more/less, head, tail), count (wc)
There are two basic types of files, text files and binary files. A lot of file formats in bioinformatics belong to simple text files, so the basic viewing/processing them is essential. Never ever do not try to open big text files (say few GB FASTQ file) in an editor, be it an editor on Linux or Microsoft Word...
Basic checking of the file content:
more myfile04.txt #displays content of the file screen by screen less myfile04.txt #same as above but you can scroll back head myfile04.txt #displays just the first 10 top lines of the file head -100 myfile04.txt #displays the first 100 lines tail myfile04.txt #displays just the last 10 lines of the file tail -100 myfile04.txt #displays the last 100 lines of the file wc myfile04.txt #newline, word, and byte counts for the file wc -l myfile04.txt # just count the lines of the file
Using these commands is very useful for basic sanity check, so we can be sure that the FASTA file looks OK, that we have X lines in the file etc. First practical NGS application:
Q: how many reads do we have in our FASTQ file A: count the lines in FASTQ file, divide by 4 (each sequence occupies 4 lines)
pattern matching, redirection and piping
Instead of using full name of a file each time, we can substitute part of the file name with special characters. Say we want to list the names all the files with .txt suffix ( myfile01.txt, myfile02.txt, etc. ) in a directory. And lets say we have about 500 of these:
ls *.txt
On Linux we can capture the output of one simple command (assuming it produces a text output) and do something useful with it, instead of just reading it from the screen. If we want to count how many such files we got, we can pipe "|" (vertical line, not the letter "I") the output of the ls command to wc command:
ls *.txt | wc -l #displays the number of .txt files in this directory
We can also redirect the output to a file, creating i.e. list of txt files with names starting with some letter (redirection with single ">":
ls m*.txt > list_of_txt_files_starting_with_m.fof #we create a new file listing m*.txt files
Or we can keep on adding the text to some file, which will be created if it does not exist (redirection with double ">>"):
ls a*.txt >> list_of_txt_files_starting_with_a_or_m.fof #file is created and we get just a*.txt file names in it ls m*.txt >> list_of_txt_files_starting_with_a_or_m.fof #file exists, so we are just appending m*.txt file names to it more list_of_txt_files_starting_with_a_or_m.fof
search for strings / replace strings (grep & sed)
We can search the text files using command called "grep". Few examples:
#general pattern: grep some_word text_file_name grep ITALY my_file.txt #prints all lines containing the word ITALY grep -v ITALY my_file.txt #"reverse grep" prints all lines not containing ITALY grep -c ITALY my_file.txt #just counts the number of lines containing ITALY grep "^>" multiple_sequences.fasta #prints all FASTA headers (lines starting with ">" sign) grep -c "^>" multiple_sequences.fasta # prints number of sequences in the multiple fasta file
Instead of opening an editor and doing search and replace, we can do it on a command line, using utility called sed, and operate both on files or use pipes.
sed 's/ITALY/SPAIN/g' my_file.txt # replaces each occurrence of ITALY with SPAIN grep "^>" multiple_sequences.fasta | sed 's/>//g' # we get all FASTA headers and then remove the ">" sign, getting just the proper FASTA IDs
compressing / uncompressing files (gzip, tar)
The sequence files are often really big, and we save space and time during downloads by compressing them. There are several compression programs, but the most frequently used is gzip:
gzip some_file #we will get some_file.gz, and the original uncompressed one will be removed gunzip some_file.gz # reversing the process, we get some_file
Program sources (almost always) or data are distributed not as single file but bundled in one file with "tar" program, then compressed for easier transfers/storage.
#we downloaded program_123.tar tar xfv program_123.tar #this will unpack the content of the tar archive #we downloaded program_123.tar.gz tar xfvz program_123.tar.gz #this will unpack the content of the compressed by gzip tar archive #we want to create tar archive containing all data_001.txt to data_100.txt (assuming there are only 100such files in this directory) #pattern #tar cfv result.tar files_to_be_archived tar cfvz my data.tar data_*.txt #to create gzipped archive tar cfvz my data.tar.gz data_*.txt
checking the integrity of the files
While with fast internet access we get what we wanted most of the time (meaning: downloading data files), from time to time we can get a corrupt file for various reasons. Also when performing data analyses the common source of endless checks and rechecks is a data file with the same name but slightly different content. A great way to assure that the transferred data were not corrupted, or that we and our collaborators are talking about the same input data or results, is to perform file "fingerprinting", or calculate md5sum hashes.
#Linux md5sum file_2_fingerprint #Mac md5 bioinfo.txt MD5 (bioinfo.txt) = 06d5cd9ffb9154d7912350250e99951b
In short, this string is strongly influenced even by minuscule changes in the file (both text and binary, including software programs), and for practical reasons if 2 hashes are identical then the content of the files is also identical.
For more info about md5sum: http://en.wikipedia.org/wiki/Md5sum
awk in few minutes
Awk is a simple programing language used for basic text processing. It is still being used because often it is faster to write and execute a command in awk than write small small script in more advanced languages, such as perl, or python. The basic concept is that awk splits the lines of the text into individual words/numbers (== great for text files in a column form), which we can then access using $column_number notation
awk '{print $1}' my_data.txt # prints just first column of the file awk '{print $1"_my_new_label"}' # prints first column but also "_my_new_label" string just after it
We can perform simple calculations, (assuming we have columns of numbers).
#this will simply sum all the numbers from the first column then print that number awk '{ sum+=$1} END {print sum}' my_data.txt
Where to go from there
more information
All the commands used above have multiple options impossible to cover during the short introduction. Once you know the name of the program, you can learn about it functions and usage reading installed manual:
man ls man grep
The recommended reference book is:
Unix Power Tools, 3rd Edition
By Jerry Peek, Shelley Powers, Tim O'Reilly, Mike Loukides
Publisher: O'Reilly Media
Released: October 2002
Pages: 1156
shell scripts
Sometimes we need to do the same thing on a set of files, like submitting a hundred jobs to a computer cluster using the same program (lets say for mapping FASTQ files) but with different input data (FASTQ files) and differently named outputs (BAM files). For doing it we need to first create 100 individual "program files" (== shell scripts), here called "map_script" i.e. using python (next section). But instead of submitting the jobs one by one we can do instead on a command line:
for file in map_script*. #press enter do #press enter qsub $file #press enter done #press enter
Python
Despite that there is a lot of bioinformatics software written in Perl (ENSEMBL, GBrowse, BioPerl), Python is a good choice as a first (and maybe even only) programming language you need for day to day bioinformatics tasks. It is easier to learn and read than Perl, but still fast enough to deal with i.e. FASTQ files.
There are two versions of Python, 2.7 and new Python 3 series which differ in subtle ways (Python 3 is an improvement). Because of the legacy software, it is still safer for time being to use the older version. One thing which distinguishes Python from other languages is that spaces/tabs/line ends are syntactic elements crucial for the language. In short, instead if using () to mark the beginning and the end of some function, Python uses indentations (tab or spaces, by default 4) to mark code blocks. With almost any programming editor, keeping track of these is painless.
- multiple file mover/renamer
#!/usr/bin/env python """ example_file_renamer.py This is just and example how to rename a bunch of files using external mv Renames all mapping_result_001.bam to mapping_result_001.bwa_cow_ENS73.bam """ import glob, os #in the pattern below the ??? stand for any 3 characters which must occur at that position in file name input_file_pattern = "mapping_result_???.bam" #glob.glob below is a way to get back a list of files matching the pattern. for input_file_name in glob.glob(input_file_pattern): #here we remove the last 3 characters from the input_file_name, then we add the "bwa_cow_ENS73.bam" to it output_file_name = input_file_name[:-3] + "bwa_cow_ENS73.bam" #here we construct the whole command to be executed. Observe extra space needed between names if the input and output files command = "mv " + input_file_name + " " + output_file_name #this is an idiom using os library to execute command os.system(command)
- Fasta header fixer
#!/usr/bin/env python """ fasta_header_fixer.py Comment: this script simplifies names of sequences in FASTA file limiting them to just first name Usage: fasta_header_fixer.py some_fasta_file_I want_to_transform > some_fasta_file_I want_to_transform.fixed_file """ import sys input_fasta_fn = sys.argv[1] #input_fasta_fn ="some_fasta_file_I want_to_transform" for line in open(input_fasta_fn).readlines(): if line[0] == ">": new_fasta_header = line.split()[0] print new_fasta_header else: print line[:-1]
- XXX ??? some mapper runner ??? XXX
computer clusters
Individual workstations or even multi CPU servers are not good enough for running hundred of CPU-demanding jobs. For that we have computer clusters, where we use tens or hundreds of computer nodes. To distribute loads on such clusters we use special software maintained by system admins, i.e. Sun Grid Engine (SGE). It takes care that not all jobs are run at once the moment they are submitted by the user, and it distributes the jobs among the nodes balancing the loads. Because individual cluster installations at your institutions are likely different from each other (they may use different software than SGE), this section is mostly for convincing you that whenever you have some cluster you can use, you should investigate how you can use it and run your next generation sequencing jobs on them. There are few concepts you need to know about typical cluster configuration and how it needs to be taken into account:
- clusters are often partitioned into smaller entities, so for example one person from one group can not block the use of the cluster for the whole institution. These partitions are being called queues.
- cluster jobs may have differer in their requirements. I.e genome assembly jobs often require large memory servers, and may take a week or more to complete. On the other end we can have tasks which are trivial to split into say 50 separate jobs by the user, and which will finish in minutes or few hours. So cluster admins often create separate queues for such different tasks, and may reserve small group on nodes for each research group, so the most urgent/or the longest running tasks can be run that way.
- It is a good policy to require that all individual jobs executing threads/processes on several CPUs in parallel should be labeled as such and wait their turn until node with required number of CPUs becomes free.
- same thing is valid for memory usage
- often jobs without information about number of CPUs/RAM requirements will have set some default values (i.e. 1 CPU) by the admins
Each job run on the cluster creates two files, capturing the output of the job which normally is printed on the screen. One is for standard messages ("mapping reads from file XYZ") other for error messages ("file XYZ not found, terminating"). These are crucial for debugging. The naming convention is as follows:
#two jobs we submitted to run bwa_map_01.sh bwa_map_02.sh #These will receive job numbers once they start running, ie: 12345 bwa_map_01.sh 12346 bwa_map_02.sh #the standard output files will be names: bwa_map_01.sh.o12345 bwa_map_02.sh.o12346 #the standard error files will be names: bwa_map_01.sh.e12345 bwa_map_02.sh.e12346 #if we re-run these scripts, the job numbers will be different, so we can get: bwa_map_01.sh.o24689 bwa_map_02.sh.o23690 bwa_map_01.sh.e24689 bwa_map_02.sh.e23690
Caveat: these files may be created in your home directory, not where the scripts and data are, when you forgot to specify -cwd during submitting your jobs
#submit a single job to a cluster to the mygroup_cluster_queue queue (with default values). #Change to the current working directory (one from which the script was submitted qsub -q mygroup_cluster_queue -cwd some_script.sh #monitor the status of your jobs on the cluster qmon #the typical output will look like this XXXX get cluster qmon output example XXXX #delete job with a number 23456 submitted to a cluster qdel 23456
Tip: it makes sense that when submitting different groups of jobs to a cluster you use different script names. If something goes wrong it will be easier to find jobs without a chance of running to completion, nd remove them.
Lets say we have 50 jobs bwa_map_001 to bwa_map_050 and another 30 jobs bowtie_map_001 to bowtie_map_030. We want to remove all our still running or submitted and waiting bowtie_map jobs, but do not touch bwa_map jobs. Here is the command:
qmon | grep myuser_name | grep bowtie_map | awk '{print $1}' | xargs qdel
Explanation of the steps:
- qmon: we list our jobs running or waiting
- grep myuser_name: just keep the lines listing the jobs, not qmon extra lines
- grep bowtie_map : we keep lines containing that string (our jobs to delete). Keep in mind that qmon does not have space to display print the whole name, so we have to check first that such string is present in qmon listing
- awk '{print $1}': we get just the first qmon column (job number)
- xargs : special command telling the next command that the parameter will be passed from the pipeline
- qdel : delete cluster job with a given job number
FASTQ
Format and quality checks
Already in the 90ties when all sequencing was being done using Sanger method, the big breakthrough in genome assembly was when individual bases in the reads (ACTG) were assigned some quality values. In short, some parts of sequences had multiple bases with a lower probability of being called right. So it makes sense that matches between high quality bases are given a higher score, be it during assembly or mapping that i.e. end of the reads with multiple doubtful / unreliable calls. This concept was borrowed by Next Generation Sequencing. While we can hardly read by eye the individual bases in some flowgrams, it is still possible for the Illumina/454/etc. software to calculate base qualities. The FASTQ format, (usually files have suffixes .fq or .fastq) contains 4 lines per sequence:
- sequence name (should be unique in the file)
- sequence string itself with ACTG and N
- extra line starting with "+" sign, which contained repeated sequence name in the past
- string of quality values (one letter/character per base) where each letter is translated in a number by the downstream programs
Here it is how it looks:
@SRR867768.249999 HWUSI-EAS1696_0025_FC:3:1:2892:17869/1 CAGCAAGTTGATCTCTCACCCAGAGAGAAGTGTTTCATGCTAAGTGGCAGTTTCTGGTGCAGAACAGTTCTGCAATGAGGGAGGAGGCAGAAAACATAAGTGTGTAATAAGGCAACCTGC + IHIIHDHIIIHIIIIIIHIIIDIIHGGIIIEIIIIIIIIIIIIGGGHIIIHIIIIIIBBIEDGGFHHEIHGIGEGHEBCHDBFC>CBCCECEEAAAAEEE:B@B@BBB;B;@;@BAE@A@
Unfortunately Solexa/Illumina did not follow the same quality encoding as people doing Sanger sequencing, so there are few iterations of the standard, with quality encodings containing different characters. For the inquisitive: http://en.wikipedia.org/wiki/FASTQ_format#Quality
What we need to remember from it, that we must know which quality encoding we have in our data, because this is an information required by mappers, and getting it wrong will make our mappings either impossible (some mappers may quit when encountering wrong quality value) or at best unreliable.
For that, if we do not have direct information from the sequencing facility which version of the Illumina software was used, we can still find it out if we investigate the FASTQ files themselves. Instead of going by eye, we use a program FastQC. For the best results/full report we need to use the whole FASTQ file as an input, but for quick and dirty quality encoding recognition using 100K of reads is enough:
head -400000 my_reads.fastq > 100K_head_my_reads.fastq fastqc 100K_head_my_reads.fastq #we got here 100K_head_my_reads.fastq_fastqc/ directory grep Encoding 100K_head_my_reads.fastq_fastqc/fastqc_data.txt #output: Encoding Sanger / Illumina 1.9
Types of data
- read length
from 35bp in some old Illumina reads to 250+ in MiSeq. The current sweet spot is between 70-120bp.
- single vs paired
Just one side of the insert sequenced or sequencing is done from both ends. Single ones are cheaper and faster to produce, but paired reads allow for more accurate mapping, detection of large insertions/deletions in the genome.
Most of the time forward and reverse reads facing each other end-to-end are
- insert length
With the standard protocol, the inserts are anywhere between 200-500bp. Sometimes especially for de novo sequencing, insert sizes can be smaller (160-180bp) with 100bp long reads allowing for overlap between ends of the reads. This can improve the genome assembly (i.e. when using Allpaths-LG assembler requiring such reads). Also with some mappers (LAST) using longer reads used to give better mappings (covering regions not unique enough for shorter reads) than 2x single end mapping. With paired end mappings the effects are modest.
Program for combining overlapping reads: FLASH: http://ccb.jhu.edu/software/FLASH/
For improving the assembly or improving the detection of larger genome rearrangements there are other libraries with various insert sizes, such as 2.5-3kb or 5kb and more. Often sequencing yields from such libs are lower than from the conventional ones.
quality checking (FastQC)
It is always a good idea to check the quality of the sequencing data prior to mapping. We can analyze average quality, over-represented sequences, number of Ns along the read and many other parameters. The program to use is FastQC, and it can be run in command line or GUI mode.
- good quality report:
- bad quality FastQC report
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/bad_sequence_fastqc/fastqc_report.html
trimming & filtering
Depending on the application, we can try to improve the quality of our data set by removing bad quality reads, clipping the last few problematic bases, or search for sequencing artifacts, as Illumina adapters. All this makes much sense for de novo sequencing, were genome assemblies can be improved by data clean up. It has a low priority for mapping, especially when we have high coverage. Bad quality reads etc. will simply be discarded by the mapper.
- Tagdust
Tagdust is a program for removing Illumina adapter sequences from the reads containing them. Such reads containing 6-8 bases not from genome will be impossible to map using typical mappers having often just 2 mismatch base limit. Tagdust works in an unpaired mode, so when using paired reads we have to "mix and match" two outputs to allow for paired mappings.
tagdust -o my_reads.clean.out.fq -a my_reads.artifact.out.fq adapters.fasta my_reads.input.fq
source of published FASTQ data: Short Read Archive vs ENA
While we will often have our data sequenced in house/provided by collaborators, we can also reuse sequences made public by others. Nobody does everything imaginable with their data, so it is quite likely we can do something new and useful with already published data, even if treating it as a control to our pipeline. Also doing exactly the same thing, say assembling genes from RNASeq data but with a newer versions of the software and or more data will likely improve on the results of previous studies. There are two main places to get such data sets:
- NCBI Short Read Archive / Taxonomy Browser:
- European Nucleotide Archive
Which one to use? ENA is easier as you get gzipped fastq files not SRA archives requiring extra processing, sometimes painful (at one stage the funding for SRA programs was cut). But NCBI tools may have better interface at times, so you can search for interesting data set at NCBI, then store the names of experiments and download fastq.gz from ENA.
Genomic fasta and gtf/gff gene annotation
ENSEMBL together with UCSC are two "clearing houses" when it comes to genome assembly and annotation. For mot of the genomes at least the genome versions (== sequences) are identical, but from time to time we can encounter a switch, like with bovine genome (UMD 3.1 @ENSEMBL and Baylor Btau 4.6.1 @UCSC). While it can be that you will have several releases of ENSEMBL with unchanged genome and annotation, it is still good idea to always include in the protocol / directories & file names the version used for the mappings/analyzes.
- Side note*
Since we are EU project, we tend to use ENSEMBL for the vast majority of the analyzes. There are some tools / data which are easier to use at UCSC, i.e. liftOver utility, for "mapping" genomic intervals from one mammalian genome (usually human or mouse) to another one (like cow). This is useful when we got large set of human genomic features (say ChIPSeq peaks) and want to find corresponding regions in cow. Check: http://genome.ucsc.edu/cgi-bin/hgLiftOver
ENSEMBL data
For getting the genome and gene annotations:
1. go to http://www.ensembl.org/info/data/ftp/index.html 2. put in white on blue bar search window (default text "Filter") i.e. "chicken". # you will get just one row with multiple entries, two of our interest at the moment: * DNA (FASTA) * Gene sets (GTF) 3. Click on DNA (FASTA) # in the directory: ftp://ftp.ensembl.org/pub/release-73/fasta/gallus_gallus/dna/) we are interested in: Gallus_gallus.Galgal4.73.dna.toplevel.fa.gz Do not click on it as doing it 50x on slow WiFi will kill the connection. 4. go back in the browser to http://www.ensembl.org/info/data/ftp/index.html (with just chicken row) 5. click on GTF # you get to ftp://ftp.ensembl.org/pub/release-73/gtf/gallus_gallus file to download (again NOT NOW!) is: Gallus_gallus.Galgal4.73.gtf.gz
Keep in mind that there are several species which were not included for various reasons in the main ENSEMBL (currently 73) release, but are accessible in preENSEMBL:
http://pre.ensembl.org/index.html
This time not every species has downloadable genome, there are just few available here: ftp://ftp.ensembl.org/pub/pre/fasta/dna/
As for GTFs it is even more unordered set, with all annotations laying around: ftp://ftp.ensembl.org/pub/pre/gtf/
Still, in times of need this is probably better than no genome & no annotation.
grepping fasta headers
This was covered in the grep session, so just a quick reminder:
#count number of sequences in multiple fasta grep -c "^>" multi_fasta.fa #list sequence names in fasta, display screen by screen with "q" to escape: grep "^>" | less
fasta reformat from exonerate
Most likely well written program for short read mapping should cope with long fasta headers:
>GI12345|QW76543 bovine contig from Angus containing genes X, Y and Z
or sequences with one chromosome per line. Still, it is in general a good idea to simplify and reformat multiple fasta genomic draft sequences to avoid potential problems problems down the road. For fixing FASTA headers (assuming that the first part of the header is unique), use Python script from the above Python section.
For reformatting sequences so that all sequences will have 70 columns:
fastareformat input.fa > output.fa
GTF / GFF annotation formats
For storing information about multiple, often genomic features the most comonly used format are few versions of GFF, in particular GTF. The GTF (General Transfer Format) is identical to GFF version 2.
Section adopted from: http://www.ensembl.org/info/website/upload/gff.html
GTF Fields:
seqname - name of the chromosome or scaffold; chromosome names can be given with or without the 'chr' prefix. source - name of the program that generated this feature, or the data source (database or project name) feature - feature type name, e.g. Gene, Variation, Similarity start - Start position of the feature, with sequence numbering starting at 1. end - End position of the feature, with sequence numbering starting at 1. score - A floating point value. strand - defined as + (forward) or - (reverse). frame - One of '0', '1' or '2'. '0' indicates that the first base of the feature is the first base of a codon, '1' that the second base is the first base of a codon, and so on.. attribute - A semicolon-separated list of tag-value pairs, providing additional information about each feature. Sample GFF output from Ensembl export: ===Example === X Ensembl Repeat 2419108 2419128 42 . . hid=trf; hstart=1; hend=21 X Ensembl Repeat 2419108 2419410 2502 - . hid=AluSx; hstart=1; hend=303 X Ensembl Repeat 2419108 2419128 0 . . hid=dust; hstart=2419108; hend=2419128 X Ensembl Pred.trans. 2416676 2418760 450.19 - 2 genscan=GENSCAN00000019335 X Ensembl Variation 2413425 2413425 . + . X Ensembl Variation 2413805 2413805 . + .
Mapping genomic reads
overview of mappers
There are tens of different mappers for aligning millions of short reads to genomes. There are huge differences in speed, memory usage, handling of errors in the read. We will take a look at just few, most commonly used by various pipelines. For the time being we will treat these programs as black boxes, not going into algorithms used inside.
We need reference genome (FASTA) from the same species to which we will map our reads, and short read data (Illumina FASTQ). Before mapping we need to do some basic checks.
checking the genome
If you get the genome for canonical species from ENSEMBL (cow, chicken, pig) you can trust that it contains proper number of chromosomes and mitochondrial sequences. This is not the case with draft genomes available from individual research groups. It is always a good idea to look at number and names of individual contigs (see grep section), and check that i.e sequences are present. In case of genomes lacking certain sequences we have bigger chances to map reads to wrong locations, which in turn will give us wrong coverage, SNPs or expression in case of RNA-Seq.
checking reads
See FastQC above and Tagdust.
Before we can use the genome for mapping we have to transform it into a format specific for each of the mappers allowing for much faster search and lower memory usage. This is often called indexing, but to make things worse indexing fasta with samtools is not the same as indexing with bwa, bowtie etc.
bwa
BWA is a the default mapper used by state of the art SNP calling GATK pipeline. There are some mappers which on some statistics may be better or equal but faster than BWA, but it is still a safe choice for doing genetic mapping. The main problem of BWA is mapping of paired reads: once one read is mapped to a good location, the second read seems to be placed close to this read (taking into account the insert size) even if the mapping would be very doubtful. This may not be a problem for GATK, since mapping qualities and flags are being accounted for, but one should keep this in mind when doing any analysis of the mapping results on your own. Currently BWA can use 3 different algorithms, each one with some limits and strong points. Here is the overview:
- Illumina reads up to 100bp: bwa-backtrack (the legacy bwa)
- sequences from 70bp up to 1Mbp:
There are two algorithms for these: BWA-SW (Smith Waterman) and BWA-MEM(seeding alignments with maximal exact matches (MEMs) and then extending seeds with the affine-gap Smith-Waterman algorithm (SW))
Please note that BWA-SW requires different algorithm for indexing the genome. The default indexing algorithm is called IS.
#creating genome index bwa index ref.fa #mapping single end reads using MEM algorithm bwa mem -p ref.bwa_is ref.fa reads.fq > short_reads.bwa_mem.sam #mapping paired end reads using MEM algorithm bwa mem ref.bwa_is reads_1.fq reads_2.fq > reads_12.bwa_mem.sam #mapping single and reads bwa aln ref.bwa_is short_read.fq > short_read.bwa_aln.sai bwa samse ref.bwa_is short_read.bwa_aln.sai short_read.fq > short_read.bwa_aln.sam #mapping paired reads bwa aln ref.bwa_is short_read_1.fq > short_read_1.bwa_aln.sai bwa aln ref.bwa_is short_read_2.fq > short_read_2.bwa_aln.sai bwa sampe ref.bwa_is short_read_1.bwa_aln.sai short_read_2.bwa_aln.sai short_read_1.fq short_read_2.fq > short_read_12.bwa_aln.sam #mapping long reads using bwasw algorithm bwa index -p ref.bwa_sw -a bwtsw ref.fa bwa bwasw ref.bwa_sw long_read.fq > long_read.bwa_sw.sam
The mode currently recommended for mapping by BWA manual and the leading SNP calling software called GATK is MEM.
last
This is less popular but sometimes quite useful mapper reporting unique mappings only. It can handle large number of mismatches and it simply remove the non-matching parts of the read, as long as what is left is sufficient to secure unique mapping. It can also be used to map very long reads, and even genome to genome (but then one has to index the genome differently). Standard usage:
lastdb -mXXXXX last_databse_name fasta_file lastal -QXXX last_databse_name input_reads
- mapping steps (for each mapper)
- genome indexing
- mapping
- +/- postprocessing
SAM and BAM file formats
The SAM file format serves to store information about result of mapping of reads to the genome. It starts with a header, describing the format version, sorting order (SO) of the reads, genomic sequences to which the reads were mapped,
- Analyzing BAM files
- sorting / indexing
During the mapping reads are mapped on first in first out, so the output is ordered by order of reads not the position along the chromosomes. Querring such BAM files is very inefficient, and the files themselves are unnecessary large. So every BAM file to be used downstream needs to be first sorted, and then indexed. BAM index (.bai suffix) is just for external programs to access a given part of the genome even more effectively. For sorting and indexing we will use samtools:
samtools sort unsorted_input.bam sorted_output #notice no bam suffix!!! samtools index sorted_output.bam #creates sorted_output.bam.bai
- viewing the mappings in IGV
IGV is a java program primarily for viewing mappings of short reads to a genome. But it can also be used for viewing SNPs (VCF files), genome anmotation or even genom-2-genome alignment (not a typical usage). The order of steps:
- start IGV (it needs to be started specifying the amount of RAM being used by the program). This depends in the coverage, number of BAM files oppened at the same time. In short, more RAM assigned, faster scrolling.
- select exact the same version of te genome with contigs named also the same way as in your BAM files
- open BAM files (need to be sorted and indexed), plus any annotation you may need.
tools for processing BAM files
- samtools
- picard
- bamtools
getting mapping stats
- extracting reads mapping to regions
- getting coverage info for selected regions
Detecting SNPs
- general procedure
The detection of SNPs depends on mapping, which in turn depends on the input read length, quality and coverage. Very short reads (i.e. 36bp) are hard to map to many regions.
- GATK pipeline
- other SNP calling programs [tba]
Working with VCF files
- VCF file format
- viewing VCFs in IGV
- filtering SNPs by quality
- set operations on VCF files (common SNPs, unique SNPs)
RNASeq
- caveats (ribosomal RNA contamination)
- mapping RNASeq
- tophat
- GRAPE
- creating gene models from RNASeq (cufflinks)
Additional info
Software list
Basics
- linux Ubuntu 12.04.3 vs Debian 7.1 (think about 32 vs 64 bit versions)
- java http://www.java.com/en/download/linux_manual.jsp?locale=en
Specific tools 1
- TagDust: http://genome.gsc.riken.jp/osc/english/software/src/tagdust.tgz
- fastareformat from fastareformat exonerate-2.2.0 [1]
- fixing fasta headers (gff fields) with python? small script
- GEM [2]
- CAVEAT: (problem with cores on different laptops...)
http://sourceforge.net/projects/gemlibrary/files/gem-library/Binary%20pre-release%203/
- BWA http://sourceforge.net/projects/bio-bwa/files/
- Stampy http://www.well.ox.ac.uk/~gerton/software/Stampy/stampy-1.0.22r1848.tgz
- last http://last.cbrc.jp/ (the 362 versiona has split and splice-mapping options)
- bowtie http://bowtie-bio.sourceforge.net/bowtie2/index.shtml (bowtie2)
- samtools http://sourceforge.net/projects/samtools/files/
- picard http://sourceforge.net/projects/picard/files/
- IGV/ IGVtools http://www.broadinstitute.org/software/igv/download
- bamtools https://github.com/pezmaster31/bamtools
- requires cmake: http://www.cmake.org/files/v2.8/cmake-2.8.12.tar.gz (or apt get)
- bedtools http://code.google.com/p/bedtools/downloads/list
- GATK http://www.broadinstitute.org/gatk/auth?package=GATK (download yourself: license!)
- vcftools http://sourceforge.net/projects/vcftools/files/
Specific tools 2/RNA-Seq
- tophat http://tophat.cbcb.umd.edu/
- cufflinks http://cufflinks.cbcb.umd.edu/ (may require Boost libs!)
- GEMtools https://github.com/gemtools/gemtools
Vagrant fixes
For X11 forwarding the Vagrantfile has to contain
config.ssh.forward_x11 = true