ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data.

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs.

A random forest classifier for detecting rare variants in NGS data from viral populations.

We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of -mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies -mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of -mers.

A Generalized Lattice based Probabilistic Approach for Metagenomic Clustering.

Metagenomics involves the analysis of genomes of microorganisms sampled directly from their environment. Next Generation Sequencing allows a high-throughput sampling of small segments from genomes in the metagenome to generate reads. To study the properties and relationships of the microorganisms present, clustering can be performed based on the inherent composition of the sampled reads for unknown species.