Biology subjects

Shirali Hossein Zade, R.

Publications and source records attributed to Shirali Hossein Zade, R..

3 recordsLinked to original sources

bioRxiv2023

When do longer reads matter? A benchmark of long read de novo assembly tools for eukaryotic genomes

BackgroundAssembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) have overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects. ResultsWe benchmarked state-of-the-art long-read de novo assemblers, to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 13 real and 72 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio CLR, PacBio HiFi, and ONT sequencing to evaluate the assemblers. We include five commonly used long read assemblers in our benchmark: Canu, Flye, Miniasm, Raven and Redbean. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies, and report that read length can, but does not always, positively impact assembly quality. ConclusionsOur benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results shows that overall Flye is the best-performing assembler, both on real and simulated data. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.

bioinformatics↗

bioRxiv2022

HAT: Haplotype Assembly Tool using short and error-prone long reads

MotivationHaplotypes are the set of alleles cooccurring on a single chromosome and inherited together to the next generation. Because a monoploid reference genome loses this co-occurrence information, its usability is limited to associate phenotypes with allelic combinations of genotypes. Therefore, methods to reconstruct the complete haplotypes from DNA sequencing data are crucial. Recently, several attempts have been made at haplotype reconstructions, but significant limitations remain. High-quality continuous haplotypes cannot be created reliably, particularly when there are few differences between the homologous chromosomes. ResultsHere, we introduce HAT, a haplotype assembly tool that exploits short and long reads along with a reference genome to reconstruct haplotypes. HAT tries to take advantage of the accuracy of short reads and the length of the long reads to reconstruct haplotypes. We tested HAT on the aneuploid yeast strain Saccharomyces pastorianus CBS1483 and multiple simulated polyploid data sets of the same strain, showing that it outperforms existing tools. Availabilityhttps://github.com/AbeelLab/hat/ Contactt.abeel@tudelft.nl

bioinformatics↗

bioRxiv2017

AntAngioCOOL: An R Package for Computational Detection of Anti-Angiogenic Peptides

Angiogenesis inhibition research is a cutting edge in angiogenesis-dependent disease therapy, and especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the cancer treatment field. In the current study we propose an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17000 features have been extracted to encode the peptides. However, finally, more than 2000 informative features have been selected to train the classifiers. According to the obtained results AntAngioCOOL can effectively predict anti-angiogenic peptides: this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/.

bioinformatics↗