gauravcodepro / trinity-blast-machine-learning

from trinity assembly to deep learning.

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trinity-blast-machine-learning

a scalable and faster implementation of the genome and the transcriptome annotations for large scale sequencing datasets. a parallelly implemented python function for the API based blast analysis of the transcripts and provides a faster and comprehensive approach to the blast analysis and filter analysis based on the similarity score. It takes a transcriptome assembly file and provides a transcriptome wide dataframe as well as the analysis plus the accession and the sequences of the blast analysis and alignment. It also provides a complete information on the taxID and also the blast sequences. It will also write and get the accessions identified in the blast hists and also the sequences for the alignment. Give the assembled genome coding regions, or the transcripts or the genes and it will prepare the clean headers, transcript headers, blast analysis, accession analysis, taxonomy identifiers analysis.

Support for the tokenizers and sequence based machine learning.

Gaurav
Academic Staff Member
Bioinformatics
Institute for Biochemistry and Biology
University of Potsdam
Potsdam,Germany