Standard and specific compression techniques for DNA microarray images

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato's CNN-based scheme

phyBWT: Alignment-Free Phylogeny via eBWT Positional Clustering

Molecular phylogenetics is a fundamental branch of biology. It studies the evolutionary relationships among the individuals of a population through their biological sequences, and may provide insights about the origin and the evolution of viral diseases, or highlight complex evolutionary trajectories. In this paper we develop a method called phyBWT, describing how to use the extended Burrows-Wheeler Transform (eBWT) for a collection of DNA sequences to directly reconstruct phylogeny, bypassing the alignment against a reference genome or de novo assembly. Our phyBWT hinges on the combinatorial properties of the eBWT positional clustering framework. We employ eBWT to detect relevant blocks of the longest shared substrings of varying length (unlike the k-mer-based approaches that need to fix the length k a priori), and build a suitable decomposition leading to a phylogenetic tree, step by step. As a result, phyBWT is a new alignment-, assembly-, and reference-free method that builds a partition tree without relying on the pairwise comparison of sequences, thus avoiding to use a distance matrix to infer phylogeny. The preliminary experimental results on sequencing data show that our method can handle datasets of different types (short reads, contigs, or entire genomes), producing trees of quality comparable to that found in the benchmark phylogeny

Sensitive Long-Indel-Aware Alignment of Sequencing Reads

The tremdendous advances in high-throughput sequencing technologies have made population-scale sequencing as performed in the 1000 Genomes project and the Genome of the Netherlands project possible. Next-generation sequencing has allowed genom-wide discovery of variations beyond single-nucleotide polymorphisms (SNPs), in particular of structural variations (SVs) like deletions, insertions, duplications, translocations, inversions, and even more complex rearrangements. Here, we design a read aligner with special emphasis on the following properties: (1) high sensitivity, i.e. find all (reasonable) alignments; (2) ability to find (long) indels; (3) statistically sound alignment scores; and (4) runtime fast enough to be applied to whole genome data. We compare performance to BWA, bowtie2, stampy and find that our methods is especially advantageous on reads containing larger indels

Certain Adenylated Non-Coding RNAs, Including 5′ Leader Sequences of Primary MicroRNA Transcripts, Accumulate in Mouse Cells following Depletion of the RNA Helicase MTR4

RNA surveillance plays an important role in posttranscriptional regulation. Seminal work in this field has largely focused on yeast as a model system, whereas exploration of RNA surveillance in mammals is only recently begun. The increased transcriptional complexity of mammalian systems provides a wider array of targets for RNA surveillance, and, while many questions remain unanswered, emerging data suggest the nuclear RNA surveillance machinery exhibits increased complexity as well. We have used a small interfering RNA in mouse N2A cells to target the homolog of a yeast protein that functions in RNA surveillance (Mtr4p). We used high-throughput sequencing of polyadenylated RNAs (PA-seq) to quantify the effects of the mMtr4 knockdown (KD) on RNA surveillance. We demonstrate that overall abundance of polyadenylated protein coding mRNAs is not affected, but several targets of RNA surveillance predicted from work in yeast accumulate as adenylated RNAs in the mMtr4KD. microRNAs are an added layer of transcriptional complexity not found in yeast. After Drosha cleavage separates the pre-miRNA from the microRNA\u27s primary transcript, the byproducts of that transcript are generally thought to be degraded. We have identified the 5′ leading segments of pri-miRNAs as novel targets of mMtr4 dependent RNA surveillance

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Bowtie: a new ultrafast memory-efficient tool for the alignment of short DNA sequence reads to large genomes

Genome-wide analysis reveals extensive functional interaction between DNA replication initiation and transcription in the genome of trypanosoma brucei

Identification of replication initiation sites, termed origins, is a crucial step in understanding genome transmission in any organism. Transcription of the Trypanosoma brucei genome is highly unusual, with each chromosome comprising a few discrete transcription units. To understand how DNA replication occurs in the context of such organization, we have performed genome-wide mapping of the binding sites of the replication initiator ORC1/CDC6 and have identified replication origins, revealing that both localize to the boundaries of the transcription units. A remarkably small number of active origins is seen, whose spacing is greater than in any other eukaryote. We show that replication and transcription in T. brucei have a profound functional overlap, as reducing ORC1/CDC6 levels leads to genome-wide increases in mRNA levels arising from the boundaries of the transcription units. In addition, ORC1/CDC6 loss causes derepression of silent Variant Surface Glycoprotein genes, which are critical for host immune evasion

Read alignment using deep neural networks

2019 Spring.Includes bibliographical references.Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871