Biology subjects

Hafezqorani, S.

Publications and source records attributed to Hafezqorani, S..

5 recordsLinked to original sources

bioRxiv2024

ntEmbd: Deep learning embedding for nucleotide sequences

Enabled by the explosion of data and substantial increase in computational power, deep learning has transformed fields such as computer vision and natural language processing (NLP) and it has become a successful method to be applied to many transcriptomic analysis tasks. A core advantage of deep learning is its inherent capability to incorporate feature computation within the machine learning models. This results in a comprehensive and machine-readable representation of sequences, facilitating the downstream classification and clustering tasks. Compared to machine translation problems in NLP, feature embedding is particularly challenging for transcriptomic studies as the sequences are string of thousands of nucleotides in length, which make the long-term dependencies between features from different parts of the sequence even more difficult to capture. This highlights the need for nucleotide sequence embedding methods that are capable of learning input sequence features implicitly. Here we introduce ntEmbd, a deep learning embedding tool that captures dependencies between different features of the sequences and learns a latent representation for given nucleotide sequences. We further provide two sample use cases, describing how learned RNA features can be used in downstream analysis. The first use case demonstrates ntEmbds utility in classifying coding and noncoding RNA benchmarked against existing tools, and the second one explores the utility of learned representations in identifying adapter sequences in nanopore RNA-seq reads. The tool as well as the trained models are freely available on GitHub at https://github.com/bcgsc/ntEmbd

bioinformatics↗

bioRxiv2023

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

AbstractThe Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

genomics↗

bioRxiv2022

Reference-free assembly of long-read transcriptome sequencing data with RNA-Bloom2

Long-read sequencing technologies have improved significantly since their emergence. Their read lengths, potentially spanning entire transcripts, is advantageous for reconstructing transcriptomes. Existing long-read transcriptome assembly methods are primarily reference-based and to date, there is little focus on reference-free transcriptome assembly. We introduce RNA-Bloom2, a reference-free assembly method for long-read transcriptome sequencing data. Using simulated datasets and spike-in control data, we show that the transcriptome assembly quality of RNA-Bloom2 is competitive to those of reference-based methods. Furthermore, RNA-Bloom2 requires 27.0 to 80.6% of the peak memory and 3.6 to 10.8% of the total wall-clock runtime of a competing reference-free method. Finally, we showcase RNA-Bloom2 in assembling a transcriptome sample of Picea sitchensis (Sitka spruce). Since our method does not rely on a reference, it sets up the groundwork for large-scale comparative transcriptomics where high-quality draft genome assemblies are not readily available.

bioinformatics↗

bioRxiv2021

Characterization and simulation of metagenomic nanopore sequencing data with Meta-NanoSim

Nanopore sequencing is crucial to metagenomic studies as its kilobase-long reads can contribute to resolving genomic structural differences among microbes. However, platform-specific challenges, including high base-call error rate, non-uniform read lengths, and the presence of chimeric artifacts, necessitate specifically designed analytical tools. Here, we present Meta-NanoSim, a fast and versatile utility that characterizes and simulates the unique properties of nanopore metagenomic reads. Further, Meta-NanoSim improves upon state-of-the-art methods on microbial abundance estimation through a base-level quantification algorithm. We demonstrate that Meta-NanoSim simulated data can facilitate the development of metagenomic algorithms and guide experimental design through a metagenomic assembly benchmarking task.

bioinformatics↗

bioRxiv2019

Trans-NanoSim characterizes and simulates nanopore RNA-seq data

We introduce Trans-NanoSim, the first tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-seq data. Through benchmarking on sets of nanopore reads from human and mouse reference transcriptomes, we show the robustness of Trans-NanoSim in capturing the characteristics of nanopore cDNA and direct RNA reads. As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim would facilitate the rapid development of analytical tools for nanopore RNA-seq data. Trans-NanoSim is freely accessible at https://github.com/bcgsc/NanoSim

bioinformatics↗