Biology subjects

Karaoglanoglu, F.

Publications and source records attributed to Karaoglanoglu, F..

3 recordsLinked to original sources

bioRxiv2025

Accurate hybrid plasmids assembly with HyPlAs

SummaryThe increasing availability of hybrid sequencing datasets comprising both short and long reads, is radically transforming microbial genomics, with the prospect of obtaining routinely near-complete bacterial genome assemblies. However, the complete assemblies of mobile genetic elements, especially plasmids, still remain challenging. We introduce HyPlAs, an assembly pipeline specifically designed to assemble plasmids from hybrid bacterial sequencing datasets. HyPlAs main novelty is to incorporate the use of a prior classification of short-read contigs as chromosomal or plasmidic. We evaluate HyPlAs on a large set of bacterial samples and demonstrate that it outperforms its competitor Plassembler. AvailabilityHyPlAs is freely available at https://github.com/cchauve/HyPlAs.

bioinformatics↗

bioRxiv2023

TKSM: Highly modular, user-customizable, and scalable transcriptomic sequencing long-read simulator

MotivationTranscriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold standard datasets hinders the benchmarking of such tools. Therefore, simulation of LR sequencing is an important and practical alternative to enable the assessment of these tools. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs). ResultsWe present TKSM, a modular and scalable LR simulator. TKSM is designed so that each RNA modification step is targeted explicitly by a software module. This allows the user to assemble a simulation pipeline of any combination of TKSM modules to emulate the sequencing design the user is targeting. Additionally, the input/output of all the core modules of TKSM follow the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps. AvailabilityTKSM is available as an open source software at https://github.com/vpc-ccg/tksm.

bioinformatics↗

bioRxiv2018

Characterization of segmental duplications and large inversions using Linked-Reads

Many algorithms aimed at characterizing genomic structural variation (SV) have been developed since the inception of high-throughput sequencing. However, the full spectrum of SVs in the human genome is not yet assessed. Most of the existing methods focus on discovery and genotyping of deletions, insertions, and mobile elements. Detection of balanced SVs with no gain or loss of genomic segments (e.g., inversions) is particularly a challenging task. Long read sequencing has been leveraged to find short inversions but there is still a need to develop methods to detect large genomic inversions. Furthermore, currently there are no algorithms to predict the insertion locus of large interspersed segmental duplications.\n\nHere we propose novel algorithms to characterize large (>40Kbp) interspersed segmental duplications and (>80Kbp) inversions using Linked-Read sequencing data. Linked-Read sequencing provides long range information, where Illumina reads are tagged with barcodes that can be used to assign short reads to pools of larger (30-50 Kbp) molecules. Our methods rely on split molecule sequence signature that we have previously described [11]. Similar to the split read, split molecules refer to large segments of DNA that span an SV breakpoint. Therefore, when mapped to the reference genome, the mapping of these segments would be discontinuous. We redesign our earlier algorithm, VALOR, to specifically leverage Linked-Read sequencing data to discover large inversions and characterize interspersed segmental duplications. We implement our new algorithms in a new software package, called VALOR2.\n\nAvailabilityVALOR2 is available at https://github.com/BilkentCompGen/valor.

bioinformatics↗