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Biopython can read and write to a number of common sequence formats, including FASTA, FASTQ, GenBank, Clustal, PHYLIP and NEXUS. When reading files, descriptive information in the file is used to populate the members of Biopython classes, such as SeqRecord. This allows records of one file format to be converted into others.
SAMtools makes it possible to work directly with a compressed BAM file, without having to uncompress the whole file. Additionally, since the format for a SAM/BAM file is somewhat complex - containing reads, references, alignments, quality information, and user-specified annotations - SAMtools reduces the effort needed to use SAM/BAM files by ...
Indexes the genome with periodic seeds to quickly find alignments with full sensitivity up to four mismatches. It can map Illumina and SOLiD reads. Unlike most mapping programs, speed increases for longer read lengths. Yes Free, GPL [49] PRIMEX Indexes the genome with a k-mer lookup table with full sensitivity up to an adjustable number of ...
DECIPHER is a software that can be used to decipher and manage ... [12] in a genome, extract them from the genome, and export them to a file. See also ...
Variants can be annotated with information about genomic features, functional consequences, regulatory elements, and population frequencies using tools like ANNOVAR or SnpEff, [23] or custom scripts and pipeline. The output from this step is an annotation file in bed or txt format. [14]
While standard data compression tools (e.g., zip and rar) are being used to compress sequence data (e.g., GenBank flat file database), this approach has been criticized to be extravagant because genomic sequences often contain repetitive content (e.g., microsatellite sequences) or many sequences exhibit high levels of similarity (e.g., multiple genome sequences from the same species).
Genome Variation Format, with additional pragmas and attributes for sequence_alteration features GFF2/GTF had a number of deficiencies, notably that it can only represent two-level feature hierarchies and thus cannot handle the three-level hierarchy of gene → transcript → exon.
The Smith-Waterman algorithm was an extension of a previous optimal method, the Needleman–Wunsch algorithm, which was the first sequence alignment algorithm that was guaranteed to find the best possible alignment. However, the time and space requirements of these optimal algorithms far exceed the requirements of BLAST.