Genome Rearrangement Problems with Single and Multiple Gene Copies: A Review

Zeira, Ron; Shamir, Ron

doi:10.1007/978-3-030-10837-3_10

Cited by 13 publications

(8 citation statements)

References 116 publications

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“…While this level of resolution provides more power for inferring the actual CNAs, especially large-scale ones that span long genomic regions (e.g., whole-genome duplication), inferences at this level quickly become intractable. Mathematical models and algorithms for genome rearrangement problems in the presence of duplication have been introduced, e.g., [80][81][82][83], some of which are being studied in the context of cancer genomics. However, these models are mostly "gene centric, " that is, they assume loci in the genome have been delineated and number of copies, as well as gene order within each genome, are unknown.…”

Section: Evolutionary Modeling and Analysis Of Cnasmentioning

confidence: 99%

Methods for copy number aberration detection from single-cell DNA-sequencing data

et al. 2020

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Copy number aberrations (CNAs), which are pathogenic copy number variations (CNVs), play an important role in the initiation and progression of cancer. Single-cell DNA-sequencing (scDNAseq) technologies produce data that is ideal for inferring CNAs. In this review, we review eight methods that have been developed for detecting CNAs in scDNAseq data, and categorize them according to the steps of a seven-step pipeline that they employ. Furthermore, we review models and methods for evolutionary analyses of CNAs from scDNAseq data and highlight advances and future research directions for computational methods for CNA detection from scDNAseq data.

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Section: Evolutionary Modeling and Analysis Of Cnasmentioning

confidence: 99%

Methods for copy number aberration detection from single-cell DNA-sequencing data

et al. 2020

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“…Secondly, inferring a phylogeny with CNV data requires quantifying biologically motivated transition probabilities for changes in copy numbers. Towards that goal, approaches to calculate the distance between whole copy number profiles [280] are a first step. But for them, a number of challenges remain, with several of the underlying problems known to be NP-hard [280].…”

Section: Statusmentioning

confidence: 99%

Eleven grand challenges in single-cell data science

et al. 2020

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The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

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“…These models have been used in many other elds of computational biology, such as scaolding [2], reference guided genome assembly [183], phylogenomics [279], and cancer genomics [384]. Comprehensive reviews of genome rearrangement models and its applications can be found in [113,142,383,15,144,311].…”

Section: Genome Rearrangementsmentioning

confidence: 99%

Biological computation and computational biology: survey, challenges, and discussion

2021

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Biological computation involves the design and development of computational techniques inspired by natural biota. On the other hand, computational biology involves the development and application of computational techniques to study biological systems. We present a comprehensive review showcasing how biology and computer science can guide and benet each other, resulting in improved understanding of biological processes and at the same time advances in the design of algorithms. Unfortunately, integration between biology and computer science is often challenging, especially due to the cultural idiosyncrasies of these two communities. In this study, we aim at highlighting how nature has inspired the development of various algorithms and techniques in computer science, and how computational techniques and mathematical modeling have helped to better understand various elds in biology. We identied existing gaps between biolog-The discussion in this paper about the challenges and importance of lling the gaps between the biological computation and computational biology communities represents the outcome of an analysis that has been done in the 5 th Heidelberg Laureate Forum; specically during a workshop https://scilogs.spektrum.de/hlf/ experience-learn-share-heidelberg-laureate-forum/ organized by Dr. Zaineb Chelly Dagdia and mentored by Professor Stephen Smale (Fields Medal awardee). After the workshop, a collaboration was formed between Dr. Zaineb Chelly Dagdia who works on biological computation and two participants and contributors to the workshop, Pavel Avdeyev and Dr. Md. Shamsuzzoha Bayzid, who work on dierent areas in computational biology.

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Genome Rearrangement Problems with Single and Multiple Gene Copies: A Review

Cited by 13 publications

References 116 publications

Methods for copy number aberration detection from single-cell DNA-sequencing data

Methods for copy number aberration detection from single-cell DNA-sequencing data

Eleven grand challenges in single-cell data science

Biological computation and computational biology: survey, challenges, and discussion

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