Ehsan Haghshenas
Overview
Explore the profile of Ehsan Haghshenas including associated specialties, affiliations and a list of published articles.
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10
Citations
128
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Recent Articles
1.
Ford M, Haghshenas E, Watson C, Sahinalp S
iScience
. 2020 Sep;
23(9):101508.
PMID: 32896768
[This corrects the article DOI: 10.1016/j.isci.2020.100883.].
2.
Haghshenas E, Asghari H, Stoye J, Chauve C, Hach F
iScience
. 2020 Aug;
23(8):101389.
PMID: 32781410
Third-generation sequencing technologies from companies such as Oxford Nanopore and Pacific Biosciences have paved the way for building more contiguous and potentially gap-free assemblies. The larger effective length of their...
3.
CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme
Asghari H, Lin Y, Xu Y, Haghshenas E, Collins C, Hach F
Bioinformatics
. 2020 Apr;
36(12):3703-3711.
PMID: 32259207
Motivation: The ubiquitous abundance of circular RNAs (circRNAs) has been revealed by performing high-throughput sequencing in a variety of eukaryotes. circRNAs are related to some diseases, such as cancer in...
4.
Ford M, Haghshenas E, Watson C, Sahinalp S
iScience
. 2020 Feb;
23(3):100883.
PMID: 32109676
One of the remaining challenges to describing an individual's genetic variation lies in the highly heterogeneous and complex genomic regions that impede the use of classical reference-guided mapping and assembly...
5.
Malikic S, Rashidi Mehrabadi F, Ciccolella S, Rahman M, Ricketts C, Haghshenas E, et al.
Genome Res
. 2019 Oct;
29(11):1860-1877.
PMID: 31628256
Available computational methods for tumor phylogeny inference via single-cell sequencing (SCS) data typically aim to identify the most likely satisfying the (ISA). However, the limitations of SCS technologies including frequent...
6.
Haghshenas E, Sahinalp S, Hach F
Bioinformatics
. 2018 Dec;
35(1):20-27.
PMID: 30561550
Motivation: Recent advances in genomics and precision medicine have been made possible through the application of high throughput sequencing (HTS) to large collections of human genomes. Although HTS technologies have...
7.
Molnar M, Haghshenas E, Ilie L
Bioinformatics
. 2017 Oct;
34(4):678-680.
PMID: 29045591
Summary: De novo genome assembly of next-generation sequencing data is a fundamental problem in bioinformatics. There are many programs that assemble small genomes, but very few can assemble whole human...
8.
La S, Haghshenas E, Chauve C
Bioinformatics
. 2017 Oct;
33(22):3652-3654.
PMID: 29036421
Motivation: Third-generation sequencing (TGS) platforms that generate long reads, such as PacBio and Oxford Nanopore technologies, have had a dramatic impact on genomics research. However, despite recent improvements, TGS reads...
9.
Haghshenas E, Hach F, Sahinalp S, Chauve C
Bioinformatics
. 2016 Sep;
32(17):i545-i551.
PMID: 27587673
Motivation: Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and...
10.
Asadi N, Mirzaei A, Haghshenas E
IEEE Trans Cybern
. 2015 Nov;
46(12):2899-2910.
PMID: 26540725
Classification of temporal data sequences is a fundamental branch of machine learning with a broad range of real world applications. Since the dimensionality of temporal data is significantly larger than...