Genome assembly reborn: recent computational challenges
By Mihai Pop. Briefings Bioinform., 2009
414 citations recorded by the source corpus.
Subfields: Ecology, Food Science, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/3fb111a7b2fd6725085ef184592b1d6b3308dba2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Curtis Huttenhower
- Aaron E. Darling
- Xochitl C. Morgan
- Jonathan A. Eisen
- Andrew J. Tritt
- Adam Godzik
- Marc T. Facciotti
- Hengyun Lu
In Food Science
- Curtis Huttenhower
- Xochitl C. Morgan
- Aaron E. Darling
- Hengyun Lu
- Zemin Ning
- Francesca Giordano
- Nicola Segata
In Molecular Biology
- Curtis Huttenhower
- Xochitl C. Morgan
- John C. Wooley
- Ole Lund
- Aaron E. Darling
- Iddo Friedberg
- Jonathan A. Eisen
- Andrew J. Tritt
Citations in the corpus
- Ray: Simultaneous Assembly of Reads from a Mix of High-Throughput Sequencing Technologies (2010, background): “The emergence of an array of sequencing technologies makes this compromise realistic.”
- An Integrated Pipeline for de Novo Assembly of Microbial Genomes (2012, background): “For a more complete description and comparison to other assembly approaches please see [29].”
- Insights from 20 years of bacterial genome sequencing (2015, background): “Traditional genome annotation pipe-lines are no longer able to scale to the rate of sequence production and new approaches are continually being considered (Nielsen et al. 2014; Pop 2009).”
- Assembly Algorithms for Next-Generation Sequencing Data (2010, neutral): “The K-mer graph approach dates to an algorithm for Sanger read assembly [24] based on a proposal [49] for an even older sequencing technology; see [3] for review.”
- A Primer on Metagenomics (2010, background): “For recent reviews on computational assembly methods see [57,58].”
- Oxford Nanopore MinION Sequencing and Genome Assembly (2016, methodology): “Although the DBG approach is faster, OLC-based algorithms perform better for longer reads with higher base error rates [35].”
- JCVI: A versatile toolkit for comparative genomics analysis (2024, background): “Despite the availability of Biopython, which provides some building blocks for bioinformatics, the complexity of genomic data often calls for specialized, high ‐ level tools that can address the challenges of genome assembly and comparative genomics that are often more complex [12].”
- Chapter 12: Human Microbiome Analysis (2012, background): “A more feasible middle ground is emerging around maximal assemblies that capture the largest unambiguous contigs in a community [65], allowing e.”