A collection of scripts that are useful for dealing with viral RNA NGS data.
Project description
The smallgenomeutilities are a collection of scripts that is useful for dealing and manipulating NGS data of small viral genomes. They are written in Python 3 with a small number of dependencies.
Dependencies
biopython
numpy
progress
pysam
sklearn
matplotlib
Installation
The recommended way to install the smallgenomeutilities is using pip:
pip install smallgenomeutilities
Description of utilities
compute_mds
Compute multidimensional scaling for visualizing distances among reconstructed haplotypes.
convert_qr
Convert QuasiRecomb output of a transmitter and recipient set of haplotypes to a combined set of haplotypes, where gaps have been filtered. Optionally translate to peptide sequence.
convert_reference
Perform a genomic liftover. Transform an alignment in SAM or BAM format from one reference sequence to another. Can replace M states by =/X.
coverage
Calculate average coverage for a target region on a different contig.
coverage_stats
Calculate average coverage for a target region of an alignment.
extract_consensus
Build consensus sequences including either the majority base or the ambiguous bases from an alignment (BAM) file.
extract_coverage_intervals
Extract regions with sufficient coverage for running ShoRAH. Half-open intervals are returned, [start:end), and 0-based indexing is used.
extract_sam
Extract subsequences of an alignment, with the option of converting it to peptide sequences. Can filter on the basis of subsequence frequency or gap frequencies in subsequences.
extract_seq
Extract sequences of alignments into a FASTA file where the sequence id matches a given string.
mapper
Determine the genomic offsets on a target contig, given an initial contig and offsets. Can be used to map between reference genomes.
minority_freq
Extract frequencies of minority variants from multiple samples. A region of interest is also supported.
pair_sequences
Compare sequences from a multiple sequence alignment from transmitter and recipient samples in order to determine the optimal matching of transmitters to recipients.
predict_num_reads
Predict number of reads after quality preprocessing.
remove_gaps_msa
Given a multiple sequence alignment, remove loci with a gap fraction above a certain threshold.
Contributions
David Seifert <david.seifert@bsse.ethz.ch>
Susana Posada Cespedes <susana.posada@bsse.ethz.ch>
Project details
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