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Nikaein, H.

Publications and source records attributed to Nikaein, H..

2 recordsLinked to original sources

ARYANA-BS: Context-Aware Alignment of Bisulfite-Sequencing Reads

MotivationDNA methylation plays a crucial role in biological processes, including imprinting, development, inflammation, and several disorders, such as cancer. Bisulfite sequencing (BS) is the gold standard for single-base resolution in measuring DNA methylation. This process involves treating genomic DNA with sodium bisulfite, followed by polymerase chain reaction (PCR), converting unmethylated cytosines to thymines (C to T) and guanines to adenines (G to A). However, aligning reads obtained through next-generation sequencing (NGS) of the converted DNA is challenging due to the high number of mismatches caused by these conversions. Various BS aligners aim to simplify BS read alignment to conventional DNA alignment by modifying the reference genome and/or reads. Methods include three-letter alignment and wild-card alignment, each with its limitations. This work introduces a novel BS aligner, ARYANA-BS, which departs from conventional DNA aligners by considering base alterations in BS reads within its alignment engine. Leveraging well-established DNA methylation patterns in different genomic contexts, ARYANA-BS generates five indexes from the reference, aligns each read to all indexes, and selects the hit with the minimum penalty. To further enhance alignment accuracy, an optional EM step has been developed, incorporating methylation probability information in the decision-making process for the index with the minimum penalty for each read. The presented approach seeks to improve the accuracy of BS read alignment by accounting for the intricacies of DNA methylation patterns in diverse genomic contexts. ResultsOur experimental results, based on both simulated and real data, demonstrate that ARYANA-BS achieves state-of-the-art accuracy while maintaining competitive speed and memory usage. AvailabilityThe source code of ARYANA and ARYANA-BS, the read simulator for both normal and bisulfite-treated reads, SAM file analyzer which is used for post processing of the alignment penalties, and test procedures for benchmarking different aligners using simulated and real data, are publicly available in https://github.com/hnikaein/aryana. Contactsharifi@sharif.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗

Alignment of Single-Molecule Sequencing Reads by Enhancing the Accuracy and Efficiency of Locality-Sensitive Hashing

Aligning single-molecule sequencing (SMS) reads to a reference genome has been computationally challenging due to the high sequencing error rates in this technology. Short distances between consecutive errors in SMS reads confront finding seeds, subsequences of the reads with exact matches to the reference, that specifically target a unique genomic position. To overcome this issue, one can look for similarities, rather than exact matches. MinHash, a locality-sensitive hashing (LSH) scheme, measures the similarity of two sequences by listing all k-mers of each one and approximating the fraction of common k-mers between them using a family of hash functions, which usually includes hundreds to thousands of different hash functions in order to increase the measurement accuracy. MinHash is used to address various bioinformatics problems, including the assembly of SMS reads. Here, we enhance both the efficiency and accuracy of the MinHash scheme by algorithmic techniques. We use a single hash function, rather than hundreds or thousands of different hash functions as used in the other MinHash-based algorithms, without losing the accuracy. We also double the size of the seed sequences by allowing one sequencing error of any form inside a pair of k-mers, which has a significant impact on the accuracy. We show algorithm, called Aryana-LoR, outperforms the accuracy of the other existing SMS aligners in both E-coli and Human genomes. AvailabilityAryana-LoR is freely available at https://gitlab.com/hnikaein/aryana-LoR

bioinformatics↗