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Like most specialized fields, next-generation sequencing has inspired many an acronym. We are trying to keep track of those abbreviations that we heavily use. Do make us aware if something is unclear: email@example.com
Reference genomes are usually referred to by their abbreviations, such as:
- hg19 = human genome, version 19
- mm9 = Mus musculus genome, version 9
- dm3 = Drosophila melanogaster, version 3
- ce10 = Caenorhabditis elegans, version 10
for a more comprehensive list of available reference genomes and their abbreviations, see the UCSC data base.
|-seq||-sequencing||indicates that an experiment was completed by DNA sequencing using NGS|
|ChIP-seq||chromatin immunoprecipitation sequencing||NGS technique for detecting transcription factor binding sites and histone modifications (see entry "Input" for more information)|
|DNase||deoxyribonuclease I||DNase I digestion is used to determine active ("open") chromatin regions|
|HTS||high-throughput sequencing||next-generation sequencing, massive parallel short read sequencing, deep sequencing|
|MNase||micrococcal nuclease||MNase digestion is used to determine sites with nucleosomes|
|NGS||next-generation sequencing||high-throughput (DNA) sequencing, massive parallel short read sequencing, deep sequencing|
|RPGC||reads per genomic content||used to normalize read numbers (also: normalize to 1x sequencing depth), sequencing depth is defined as: (total number of mapped reads * fragment length) / effective genome size.|
|RPKM||reads per kilobase per million reads||used to normalize read numbers, the following formula is used by bamCoverage: RPKM (per bin) = number of reads per bin / ( number of mapped reads (in millions) * bin length (kb))|
For a review of popular *-seq applications, see Zentner and Henikoff, 2012
In addition to abbreviations, there are many specialized terms and different labs and people use different terms for the same thing.
|bin||window, region||For many calculations, the genome is divided into smaller chunks, for example for the calculation of read coverages. These regions can be as small as 1 bp, but really, they can be any size. We commonly use the term "bin" for those artificially created genome parts as we feel that we "store" scores (e.g. read coverages or motif scores) in them.|
|Input||--||control experiment typically done for ChIP-seq experiments (see above) - while ChIP-seq relies on antibodies to enrich for DNA fragments bound to a certain protein, the input sample should be processed exactly the same way, excluding the antibody. This way, one hopes to account for biases introduced by the sample handling and the general chromatin structure of the cells|
|read||tag||This term refers to the piece of DNA that is sequenced ("read") by the sequencers. We try to differentiate between "read" and "DNA fragment" as the fragments that are put into the sequencer tend to be in the range of 200-1000 bp of which only the first 30 to 100 bp (depending on the read length) are in fact sequenced. Most of the deepTools will not only take those 30 to 100 bp into consideration when calculating coverages, instead they will extend the reads to match the original DNA fragment size. (The original size will either be given by you or, if you used paired-end sequencing, can be calculated by the distance of two read mates).|
Data obtained from next-generation sequencing data must be processed several times. Most of the processing steps are aimed at extracting only those information that are truly needed for a specific down-stream analysis and to discard all the redundant entries. Therefore, specific data formats are often associated with different steps of a data processing pipeline. These associations, however, are by no means binding, but you should understand what kind of data is represented in which data format - this will help you to select the correct tools further down the road.
Here, we just want to give very brief key descriptions of the file, for elaborate information we will link to external websites. Be aware, that the file name sorting here is purely alphabetically, not according to their usage within an analysis pipeline that is depicted here:
- compressed, binary version of genome sequences that are often stored in FASTA
- most genomes in 2bit format can be found at UCSC
- FASTA files can be converted to 2bit using the UCSC programm faToTwoBit available for different platforms at UCSC
- more information can be found here or from UCSC
- typical file extension: .bam
- binary file format (complement to SAM)
- contains information about sequenced reads after alignment to a reference genome
- each line = 1 mapped read, with information about:
- its mapping quality (how certain is the read alignment to this particular genome locus?)
- its sequencing quality (how well was each base pair detected during sequencing?)
- its DNA sequence
- its location in the genome
- highly recommended format for storing data
- to make a BAM file human-readable, one can, for example, use the program samtools view
- for more information, see below for the definition of SAM files
- typical file extension: .bed
- text file
- used for genomic intervals, e.g. genes, peak regions etc.
- actually, there is a rather strict definition of the format that can be found at UCSC
- for deepTools, the first 3 columns are important: chromosome, start position of the region, end position of the genome
- do not confuse it with the bedGraph format (eventhough they are quite similar)
- example lines from a BED file of mouse genes (note that the start position is 0-based, the end-position 1-based, following UCSC conventions for BED files):
chr1 3204562 3661579 NM_001011874 Xkr4 - chr1 4481008 4486494 NM_011441 Sox17 - chr1 4763278 4775807 NM_001177658 Mrpl15 - chr1 4797973 4836816 NM_008866 Lypla1 +
- typical file extension: .bg, .bedgraph
- text file
- similar to BED file (not the same!), it can only contain 4 columns and the 4th column must be a score
- again, read the UCSC description for more details
- 4 exemplary lines from a bedGraph file (like BED files following the UCSC convention, the start position is 0-based, the end-position 1-based in bedGraph files):
chr1 10 20 1.5 chr1 20 30 1.7 chr1 30 40 2.0 chr1 40 50 1.8
- typical file extension: .bw, .bigwig
- binary version of a bedGraph file
- usually contains 4 columns: chromosome, start of genomic bin, end of genomic bin, score
- the score can be anything, e.g. an average read coverage
- UCSC description for more details
- typical file extension: .fasta
- text file, often gzipped (--> .fasta.gz)
- very simple format for DNA/RNA or protein sequences, this can be anything from small pieces of DNA or proteins to entire genome information (most likely, you will get the genome sequence of your organism of interest in fasta format)
- see the 2bit file format entry for a compressed alternative of the fasta format
- example from wikipedia showing exactly one sequence:
>gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus] LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX IENY
- typical file extension: .fastq, fq
- text file, often gzipped (--> .fastq.gz)
- contains raw read information (e.g. base calls, sequencing quality measures etc.), but not information about where in the genome the read originated from
- example from the wikipedia page
A FASTQ file containing a single sequence might look like this: @SEQ_ID GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT + !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 The character '!' represents the lowest quality while '~' is the highest.
- typical file extension: .sam
- should be the result of an alignment of sequenced reads to a reference genome
- each line = 1 mapped read, with information about its mapping quality, its sequence, its location in the genome etc.
- it is recommended to generate the binary (compressed) version of this file format: BAM
- for more information, see the SAM specification
- two exemplary lines
- each one corresponds to one read (named r001 and r002 here)
- the different columns contain various information about each read, e.g. which chromosome they were mapped to (here: chr1) and the left-most mapping position in the genome (here: 7 and 9 on chr1); the flag in the second column summarizes multiple information about each single read at once (in hexadecimal encoding) (see below for more information on the flag)
r001 163 chr1 7 30 8M2I4M1D3M = 37 39 TTAGATAAAGGATACTG * r002 0 chr1 9 30 3S6M1P1I4M * 0 0 AAAAGATAAGGATA *
- the flag contains the answer to several yes/no assessments that are encoded in a single number. The questions are the following ones:
- template having multiple segments in sequencing = Is the read part of a read pair?
- each segment properly aligned according to the aligner = Was the read properly paired?
- segment unmapped = Is the read unmapped?
- next segment in the template unmapped = Is the mate unmapped?
- reverse complemented = Did the read map to the reverse strand?
- next segment in the template is reversed = Did the mate map to the reverse strand?
- the first seg
- ment in the template = Is this read the first one in the pair?
- the last segment in the template = Is this read the second one in the pair?
- secondary alignment = Is this not the primary (i.e. unique optimal) alignment for the read?
- not passing quality controls = Did the read not pass the quality control?
- PCR or optical duplicate = Was this read a PCR or optical duplicate?
- for more details on the flag, see this thorough explanation or this more technical explanation
[read]: https://github.com/fidelram/deepTools/wiki/Glossary#terminology "the DNA piece that was actually sequenced ("read") by the sequencing machine (usually between 30 to 100 bp long, depending on the read-length of the sequencing protocol)" [input]: https://github.com/fidelram/deepTools/wiki/Glossary#terminology "confusing, albeit commonly used name for the 'no-antibody' control sample for ChIP experiments"