Review of General Algorithmic Features for Genome Assemblers for Next Generation Sequencers

Abstract

In the realm of bioinformatics and computational biology, the most rudimentary data upon which all the analysis is built is the sequence data of genes, proteins and RNA. The sequence data of the entire genome is the solution to the genome assembly problem. The scope of this contribution is to provide an overview on the art of problem-solving applied within the domain of genome assembly in the next-generation sequencing (NGS) platforms. This article discusses the major genome assemblers that were proposed in the literature during the past decade by outlining their basic working principles. It is intended to act as a qualitative, not a quantitative, tutorial to all working on genome assemblers pertaining to the next generation of sequencers. We discuss the theoretical aspects of various genome assemblers, identifying their working schemes. We also discuss briefly the direction in which the area is headed towards along with discussing core issues on software simplicity.

Optimal reference sequence selection for genome assembly using minimum description length principle

“…The layout helps in producing a consensus sequence, where each base in the sequence is identified by simple majority amongst the bases at that position or via some probabilistic approach. Therefore, this “Alignment-Layout-Consensus” paradigm is used by genome assemblers to infer the novel genome, [27-35]. …”

“…It begins the process by identifying a model, the “reference sequences”, most closely related to the set of reads. It then uses the set of reads to build on this model producing a model which overfits the data, the “novel genome”, [27,28,34,36-41]. The task of MDL is to identify the model that best describes the data and within comparative assembly framework the same meaning applies to finding the reference sequences that best describes the set of reads.…”

Computational Errors and Biases in Short Read Next Generation Sequencing

“…For an extensive literature on assemblers consult [54,[69][70][71][72][73]. The list of aligners is updated online [53].…”

DeepDeath: Learning to predict the underlying cause of death with Big Data

“…N-gram based models have been widely used in natural language processing [11][12][13] and bioinformatics [14,15] due to their performance and ease of implementation. In this study, we only use uni-gram features and bi-gram features.…”