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Massive sequence comparisons as a help in annotating genomic sequences.

ABSTRACT: An all-by-all comparison of all the publicly available protein sequences from plants has been performed, followed by a clusterization process. Within each of the 1064 resulting clusters-containing sequences that are orthologous as well as paralogous-the sequences have been submitted to a pyramidal classification and their domains delineated by an automated procedure à la. This process provides a means for easily checking for any apparent inconsistency in a cluster, for example, whether one sequence is shorter or longer than the others, one domain is missing, etc. In such cases, the alignment of the DNA sequence of the gene with that of a close homologous protein often reveals (in 10% of the clusters) probable sequencing errors (leading to frameshifts) or probable wrong intron/exon predictions. The composition of the clusters, their pyramidal classifications, and domain decomposition, as well as our comments when appropriate, are available from http://chlora.infobiogen.fr:1234/PHYTOPROT.

SUBMITTER: Louis A

PROVIDER: S-EPMC311131 | biostudies-literature | 2001 Jul

REPOSITORIES: biostudies-literature

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Massive sequence comparisons as a help in annotating genomic sequences.

Louis A A Ollivier E E Aude J C JC Risler J L JL

Genome research 20010701 7

An all-by-all comparison of all the publicly available protein sequences from plants has been performed, followed by a clusterization process. Within each of the 1064 resulting clusters-containing sequences that are orthologous as well as paralogous-the sequences have been submitted to a pyramidal classification and their domains delineated by an automated procedure à la. This process provides a means for easily checking for any apparent inconsistency in a cluster, for example, whether one seque ...[more]

PMID: 11435413

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Project description:BackgroundAs healthcare-related data proliferate, there is need to annotate them expertly for the purposes of personalized medicine. Crowdworking is an alternative to expensive expert labour. Annotation corresponds to diagnosis, so comparing unlabeled records to labeled ones seems more appropriate for crowdworkers without medical expertise. We modeled the comparison of a record to two other records as a triplet annotation task, and we conducted an experiment to investigate to what extend sensor-measured stress, task duration, uncertainty of the annotators and agreement among the annotators could predict annotation correctness.Materials and methodsWe conducted an annotation experiment on health data from a population-based study. The triplet annotation task was to decide whether an individual was more similar to a healthy one or to one with a given disorder. We used hepatic steatosis as example disorder, and described the individuals with 10 pre-selected characteristics related to this disorder. We recorded task duration, electro-dermal activity as stress indicator, and uncertainty as stated by the experiment participants (n = 29 non-experts and three experts) for 30 triplets. We built an Artificial Similarity-Based Annotator (ASBA) and compared its correctness and uncertainty to that of the experiment participants.ResultsWe found no correlation between correctness and either of stated uncertainty, stress and task duration. Annotator agreement has not been predictive either. Notably, for some tasks, annotators agreed unanimously on an incorrect annotation. When controlling for Triplet ID, we identified significant correlations, indicating that correctness, stress levels and annotation duration depend on the task itself. Average correctness among the experiment participants was slightly lower than achieved by ASBA. Triplet annotation turned to be similarly difficult for experts as for non-experts.ConclusionOur lab experiment indicates that the task of triplet annotation must be prepared cautiously if delegated to crowdworkers. Neither certainty nor agreement among annotators should be assumed to imply correct annotation, because annotators may misjudge difficult tasks as easy and agree on incorrect annotations. Further research is needed to improve visualizations for complex tasks, to judiciously decide how much information to provide, Out-of-the-lab experiments in crowdworker setting are needed to identify appropriate designs of a human-annotation task, and to assess under what circumstances non-human annotation should be preferred.

Dataset Information

Massive sequence comparisons as a help in annotating genomic sequences.

Publications

Massive sequence comparisons as a help in annotating genomic sequences.

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