Probabilistic model based error correction in a set of various mutant sequences analyzed by next-generation sequencing

conLSH: Context based Locality Sensitive Hashing for mapping of noisy SMRT reads

Single Molecule Real-Time (SMRT) sequencing is a recent advancement of Next Gen technology developed by Pacific Bio (PacBio). It comes with an explosion of long and noisy reads demanding cutting edge research to get most out of it. To deal with the high error probability of SMRT data, a novel contextual Locality Sensitive Hashing (conLSH) based algorithm is proposed in this article, which can effectively align the noisy SMRT reads to the reference genome. Here, sequences are hashed together based not only on their closeness, but also on similarity of context. The algorithm has O(n^ρ+1) space requirement, where n is the number of sequences in the corpus and ρ is a constant. The indexing time and querying time are bounded by On^ρ+1·lnnln1P₂ and O(n^ρ) respectively, where P₂ > 0, is a probability value. This algorithm is particularly useful for retrieving similar sequences, a widely used task in biology. The proposed conLSH based aligner is compared with rHAT, popularly used for aligning SMRT reads, and is found to comprehensively beat it in speed as well as in memory requirements. In particular, it takes approximately 24.2% less processing time, while saving about 70.3% in peak memory requirement for H.sapiens PacBio dataset.

The analysis of clonal expansions in normal and autoimmune B cell repertoires

Clones are the fundamental building blocks of immune repertoires. The number of different clones relates to the diversity of the repertoire, whereas their size and sequence diversity are linked to selective pressures. Selective pressures act both between clones and within different sequence variants of a clone. Understanding how clonal selection shapes the immune repertoire is one of the most basic questions in all of immunology. But how are individual clones defined? Here we discuss different approaches for defining clones, starting with how antibodies are diversified during different stages of B cell development. Next, we discuss how clones are defined using different experimental methods. We focus on high-throughput sequencing datasets, and the computational challenges and opportunities that these data have for mining the antibody repertoire landscape. We discuss methods that visualize sequence variants within the same clone and allow us to consider collections of shared mutations to determine which sequences share a common ancestry. Finally, we comment on features of frequently encountered expanded B cell clones that may be of particular interest in the setting of autoimmunity and other chronic conditions.

Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction

Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.