Unsupervised segmentation of continuous genomic data
By Nathan Day, Andrew Hemmaplardh, Robert E. Thurman, John A. Stamatoyannopoulos, William Stafford Noble. Bioinform., 2007
5,326 citations recorded by the source corpus.
UNLABELLED The advent of high-density, high-volume genomic data has created the need for tools to summarize large datasets at multiple scales. HMMSeg is a command-line utility for the scale-specific segmentation of continuous genomic data using hidden Markov models (HMMs). Scale specificity is achieved by an optional wavelet-based smoothing operation. HMMSeg is capable of handling multiple datasets simultaneously, rendering it ideal for integrative analysis of expression, phylogenetic and functional genomic data. AVAILABILITY http://noble.gs.washington.edu/proj/hmmseg
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/6ecd2fc867390c8d63c5ee1779ea7b9445b90372
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Citations in the corpus
- CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes (2015, background): “The quality of isolate genomes has traditionally been evaluated using assembly statistics such as N50 (Salzberg et al. 2012; Gurevich et al. 2013), while single cell and metagenomic studies have relied on the presence and absence of universal single-copy ‘marker’ genes for estimating genome complete”
- Chromatin-state discovery and genome annotation with ChromHMM (2017, methodology): “Before ChromHMM, HMMSeg 42 used a HMM to partition the genome into two states 43 .”
- Assessing genome assembly quality using the LTR Assembly Index (LAI) (2018, methodology): “In contrast, the QUAST program compares genome assembler programs by estimating misassemblies in contig blocks (1), which is limited however by the availability of a reference genome.”
- Assembly of long error-prone reads using de Bruijn graphs (2016, methodology): “Thus, the standard benchmarking approach based on comparison with the reference genome (52) is not applicable to these assemblies.”