smallgenomeutilities 0.3.0

A collection of scripts that are useful for dealing with viral RNA NGS data.

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 <[e-mail]>

• Susana Posada Cespedes <[e-mail]>