Accurate Flow Decomposition via Robust Integer Linear Programming.

Minimum flow decomposition (MFD) is a common problem across various fields of Computer Science, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous since it is obtained from noisy read coverages. Typical generalizations of the MFD problem to handle errors are based on least-squares formulations or modelling the erroneous flow values as ranges.

A safety framework for flow decomposition problems via integer linear programming.

Motivation: Many important problems in Bioinformatics (e.g. assembly or multiassembly) admit multiple solutions, while the final objective is to report only one.

Sensitive inference of alignment-safe intervals from biodiverse protein sequence clusters using EMERALD.

Sequence alignments are the foundations of life science research, but most innovation so far focuses on optimal alignments, while information derived from suboptimal solutions is ignored. We argue that one optimal alignment per pairwise sequence comparison is a reasonable approximation when dealing with very similar sequences but is insufficient when exploring the biodiversity of the protein universe at tree-of-life scale. To overcome this limitation, we introduce pairwise alignment-safety to uncover the amino acid positions robustly shared across all suboptimal solutions.

Efficient Minimum Flow Decomposition via Integer Linear Programming.

Minimum flow decomposition (MFD) is an NP-hard problem asking to decompose a network flow into a minimum set of paths (together with associated weights). Variants of it are powerful models in multiassembly problems in Bioinformatics, such as RNA assembly. Owing to its hardness, practical multiassembly tools either use heuristics or solve simpler, polynomial time-solvable versions of the problem, which may yield solutions that are not minimal or do not perfectly decompose the flow.

Whole genome sequencing of Trypanosoma cruzi field isolates reveals extensive genomic variability and complex aneuploidy patterns within TcII DTU.

Background: Trypanosoma cruzi, the etiologic agent of Chagas disease, is currently divided into six discrete typing units (DTUs), named TcI-TcVI. TcII is among the major DTUs enrolled in human infections in South America southern cone, where it is associated with severe cardiac and digestive symptoms. Despite the importance of TcII in Chagas disease epidemiology and pathology, so far, no genome-wide comparisons of the mitochondrial and nuclear genomes of TcII field isolates have been performed to track the variability and evolution of this DTU in endemic regions.