Differences in codon bias cannot explain differences in translational power among microbes

Dethlefsen, Les; Schmidt, Thomas M.

doi:10.1186/1471-2105-6-3

Cited by 12 publications

(3 citation statements)

References 92 publications

(144 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of biomedical literature mining, the bio-entities that attracted more interest were genes, proteins, cell lines, cell types, drugs, mutations and organisms or species [ 11 , 1 , 14 ]. The recognition of gene and protein mentions was addressed in several community challenges (BioCreative I, II, JNLPBA) that served to determine the state of the art methodology and systems performance [ 5 , 12 ] in addition of providing valuable datasets for developing new systems [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

CHEMDNER: The drugs and chemical names extraction challenge

et al. 2015

View full text Add to dashboard Cite

Natural language processing (NLP) and text mining technologies for the chemical domain (ChemNLP or chemical text mining) are key to improve the access and integration of information from unstructured data such as patents or the scientific literature. Therefore, the BioCreative organizers posed the CHEMDNER (chemical compound and drug name recognition) community challenge, which promoted the development of novel, competitive and accessible chemical text mining systems. This task allowed a comparative assessment of the performance of various methodologies using a carefully prepared collection of manually labeled text prepared by specially trained chemists as Gold Standard data. We evaluated two important aspects: one covered the indexing of documents with chemicals (chemical document indexing - CDI task), and the other was concerned with finding the exact mentions of chemicals in text (chemical entity mention recognition - CEM task). 27 teams (23 academic and 4 commercial, a total of 87 researchers) returned results for the CHEMDNER tasks: 26 teams for CEM and 23 for the CDI task. Top scoring teams obtained an F-score of 87.39% for the CEM task and 88.20% for the CDI task, a very promising result when compared to the agreement between human annotators (91%). The strategies used to detect chemicals included machine learning methods (e.g. conditional random fields) using a variety of features, chemistry and drug lexica, and domain-specific rules. We expect that the tools and resources resulting from this effort will have an impact in future developments of chemical text mining applications and will form the basis to find related chemical information for the detected entities, such as toxicological or pharmacogenomic properties.

show abstract

Section: Introductionmentioning

confidence: 99%

CHEMDNER: The drugs and chemical names extraction challenge

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Statistical (or machine learning) approaches rely on word distribution for discriminating term and non-term features [ 14 - 16 ]. The key to successfully train a statistical model is annotated corpora [ 17 - 20 ], and the limited availability of such gold-standard sets is one of the main difficulties. It is also challenging to choose a set of discriminating features in statistical approaches.…”

Section: Introductionmentioning

confidence: 99%

Identifying named entities from PubMed® for enriching semantic categories

Kim

Wilbur

2015

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundControlled vocabularies such as the Unified Medical Language System (UMLS®) and Medical Subject Headings (MeSH®) are widely used for biomedical natural language processing (NLP) tasks. However, the standard terminology in such collections suffers from low usage in biomedical literature, e.g. only 13% of UMLS terms appear in MEDLINE®.ResultsWe here propose an efficient and effective method for extracting noun phrases for biomedical semantic categories. The proposed approach utilizes simple linguistic patterns to select candidate noun phrases based on headwords, and a machine learning classifier is used to filter out noisy phrases. For experiments, three NLP rules were tested and manually evaluated by three annotators. Our approaches showed over 93% precision on average for the headwords, “gene”, “protein”, “disease”, “cell” and “cells”.ConclusionsAlthough biomedical terms in knowledge-rich resources may define semantic categories, variations of the controlled terms in literature are still difficult to identify. The method proposed here is an effort to narrow the gap between controlled vocabularies and the entities used in text. Our extraction method cannot completely eliminate manual evaluation, however a simple and automated solution with high precision performance provides a convenient way for enriching semantic categories by incorporating terms obtained from the literature.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0487-2) contains supplementary material, which is available to authorized users.

show abstract

“…In addition to the nucleotide substitution patterns, a competing influence on silent sites is selection acting to make protein translation more ‘efficient’ (in this context implying ‘faster’) and more accurate; although the term ‘efficiency’ is technically a misnomer [8] , we use it for sake of consistency with previous literature. Traditionally, this effect was linked to abundances of tRNA isoacceptors for a particular codon [9] , in agreement with a model where the speed of translational elongation is limited by availability of charged tRNA molecules [10] .…”

Section: Introductionmentioning

confidence: 99%

Translational Selection Is Ubiquitous in Prokaryotes

et al. 2010

View full text Add to dashboard Cite

Codon usage bias in prokaryotic genomes is largely a consequence of background substitution patterns in DNA, but highly expressed genes may show a preference towards codons that enable more efficient and/or accurate translation. We introduce a novel approach based on supervised machine learning that detects effects of translational selection on genes, while controlling for local variation in nucleotide substitution patterns represented as sequence composition of intergenic DNA. A cornerstone of our method is a Random Forest classifier that outperformed previous distance measure-based approaches, such as the codon adaptation index, in the task of discerning the (highly expressed) ribosomal protein genes by their codon frequencies. Unlike previous reports, we show evidence that translational selection in prokaryotes is practically universal: in 460 of 461 examined microbial genomes, we find that a subset of genes shows a higher codon usage similarity to the ribosomal proteins than would be expected from the local sequence composition. These genes constitute a substantial part of the genome—between 5% and 33%, depending on genome size—while also exhibiting higher experimentally measured mRNA abundances and tending toward codons that match tRNA anticodons by canonical base pairing. Certain gene functional categories are generally enriched with, or depleted of codon-optimized genes, the trends of enrichment/depletion being conserved between Archaea and Bacteria. Prominent exceptions from these trends might indicate genes with alternative physiological roles; we speculate on specific examples related to detoxication of oxygen radicals and ammonia and to possible misannotations of asparaginyl–tRNA synthetases. Since the presence of codon optimizations on genes is a valid proxy for expression levels in fully sequenced genomes, we provide an example of an “adaptome” by highlighting gene functions with expression levels elevated specifically in thermophilic Bacteria and Archaea.

show abstract

Differences in codon bias cannot explain differences in translational power among microbes

Cited by 12 publications

References 92 publications

CHEMDNER: The drugs and chemical names extraction challenge

CHEMDNER: The drugs and chemical names extraction challenge

Identifying named entities from PubMed® for enriching semantic categories

Translational Selection Is Ubiquitous in Prokaryotes

Contact Info

Product

Resources

About