TY - JOUR
T1 - LocText
T2 - relation extraction of protein localizations to assist database curation
AU - Cejuela, Juan Miguel
AU - Vinchurkar, Shrikant
AU - Goldberg, Tatyana
AU - Prabhu Shankar, Madhukar Sollepura
AU - Baghudana, Ashish
AU - Bojchevski, Aleksandar
AU - Uhlig, Carsten
AU - Ofner, André
AU - Raharja-Liu, Pandu
AU - Jensen, Lars Juhl
AU - Rost, Burkhard
PY - 2018/1/17
Y1 - 2018/1/17
N2 - BACKGROUND: The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence.RESULTS: LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot.CONCLUSIONS: LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot.
AB - BACKGROUND: The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence.RESULTS: LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot.CONCLUSIONS: LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot.
U2 - 10.1186/s12859-018-2021-9
DO - 10.1186/s12859-018-2021-9
M3 - Journal article
C2 - 29343218
SN - 1471-2105
VL - 19
SP - 1
EP - 11
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 15
ER -