MAF: An Advancement in Biopython

The computation of biological problems through python is a great insight for the biological computation. The effortful contribution of the developers leads Biopython to grow up from 1999 to till date.¹ Current development ensures the several new application of the Biopython to address the future aspects of bioinformatics and computation. In the new releases of Biopython some of the codes have been changed for better uses.²

In the Bio.AlignIO.MafIO for MAF(Multiple Alignment Format) has been improved for better endpoint searches by using inclusive co-ordinates of the end, useful for rebuilding MAF.³ In the Bio.Seq, the translation of the sequences could be done using Codon_start, transl_table fetchers available in the new release. ³ The integer-multiplication method and matching native-python string methods are able to support for some functions named seq, MutableSec, UnknownSeq etc.. ³ An alternative to Bio.pairwise2 for pair wise sequence aligner has been introduced named Bio.Align. ³ Experimentation on Bio.SearchIO results good understanding of the information regarding the flexibilities of the fetchers which results no more prompt for warnings during use. It is now able to handle or manipulate gene files for different applicable purposes using Bio.KEGG.⁴

An introduction of internal changes on previously used Bio.SeqIO was performed, in which the Seq.write and SeqRecord.format were used, where now the run-time has been reduced for the purpose of efficient use of for loop in the function for sequence handling. ³ The Bio.SeqIO using new introduction of fasta-2line can write or read two file per record fasta files. ⁴ A modules, which use NumPy is reduced the time by lacking extra compilation time but using only runtime. These changes are done by modulating the internal C code mean. ³

In the new upgrade the key NCBI-API is included and could be used as supported key within the Entrez module. ³ A custom directory could be set now for the XSD files, which leads to improvement of the read process for Entrez XML files. ³ And also the custom directory could be set for DTD files as well. For these processes the most restricted AWS Lambda now can excess for the directories. ³

The mmCIF file format was introduced in Bio.PDB which is a structure file, now also could write or convert between PDB and mmCIF. ⁴ Update of the list of restriction enzymes in Bio.restriction done by the name REBASES. ⁴ New codon table have been introduced, where the stop codons arranged for context dependent encoding for user friendly use of the codon even as aminoacid. ⁴ A wrong parameter named ‘fuzznuc wrapper’ has been refined and introduced a new parameter, which is under the Bio.Emboss.Applications. ⁴

Use of biopython is not limited to only the scripting and previous servers and tools, this is using in new tools for biological purposes. ⁴ In Next Generation Sequencing the corseq is an efficient tool for finding favored codons from the NGS reads, which is based on Biopython.⁵ The MOODS software is developed for the motif matching is developed based on Biopython in the recent developments.⁶ Similarly the Fasta validator is Biopython based program.⁷ Avery newly released Biopython based program named ‘PyPathway’ is developed for the parpose of analysis and visualization of biological networks and functional enrichment analysis.⁸

Referances:

1. Brad Chapman, The Biopython Project: Philosophy, functionality and facts. 11 March 2004.
2. https://biopython.org/wiki
3. https://news.open-bio.org/
4. https://news.open-bio.org/2018/04/04/biopython-1-71-released/
5. Salvatore Camiolo and Andrea Porceddu. corseq: fast and efficient identification of favoured codons from next generation sequencing reads.
6. Janne H Korhonen  , Kimmo Palin, Jussi Taipale, Esko Ukkonen. Fast motif matching revisited: high-order PWMs, SNPs and indels. Bioinformatics, Volume 33, Issue 4, 15 February 2017, Pages 514–521.
7. Jost Waldmann, Jan Gerken, Frank Oliver Glöckner. FastaValidator: an open-source Java library to parse and validate FASTA formatted sequences.
8. Xu Y , Luo XC . PyPathway: Python Package for Biological Network Analysis and Visualization. J Comput Biol. 2018 May; 25(5):499-504. doi: 10.1089/cmb.2017.0199. Epub 2018 Apr 11.

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