Genomics & informatics最新文献_第9页

Trends in Genomics & Informatics: a statistical review of publications from 2003 to 2018 focusing on the most-studied genes and document clusters 基因组学和信息学趋势：2003年至2018年出版物的统计综述，重点关注研究最多的基因和文献集群

Genomics & informatics Pub Date : 2019-09-01 DOI: 10.5808/GI.2019.17.3.e25

Jihyeon Kim, Hee-Jo Nam, Hyun-Seok Park

引用次数: 1

Optimization of a microarray for fission yeast 裂变酵母微阵列的优化

Genomics & informatics Pub Date : 2019-09-01 DOI: 10.5808/GI.2019.17.3.e28

Dong-Uk Kim, Minho Lee, Sangjo Han, Miyoung Nam, Sol Lee, Jaewoong Lee, Jihye Woo, Dongsup Kim, K. Hoe

{"title":"Optimization of a microarray for fission yeast","authors":"Dong-Uk Kim, Minho Lee, Sangjo Han, Miyoung Nam, Sol Lee, Jaewoong Lee, Jihye Woo, Dongsup Kim, K. Hoe","doi":"10.5808/GI.2019.17.3.e28","DOIUrl":"https://doi.org/10.5808/GI.2019.17.3.e28","url":null,"abstract":"Bar-code (tag) microarrays of yeast gene-deletion collections facilitate the systematic identification of genes required for growth in any condition of interest. Anti-sense strands of amplified bar-codes hybridize with ~10,000 (5,000 each for up- and down-tags) different kinds of sense-strand probes on an array. In this study, we optimized the hybridization processes of an array for fission yeast. Compared to the first version of the array (11 µm, 100K) consisting of three sectors with probe pairs (perfect match and mismatch), the second version (11 µm, 48K) could represent ~10,000 up-/down-tags in quadruplicate along with 1,508 negative controls in quadruplicate and a single set of 1,000 unique negative controls at random dispersed positions without mismatch pairs. For PCR, the optimal annealing temperature (maximizing yield and minimizing extra bands) was 58℃ for both tags. Intriguingly, up-tags required 3× higher amounts of blocking oligonucleotides than down-tags. A 1:1 mix ratio between up- and down-tags was satisfactory. A lower temperature (25℃) was optimal for cultivation instead of a normal temperature (30℃) because of extra temperature-sensitive mutants in a subset of the deletion library. Activation of frozen pooled cells for >1 day showed better resolution of intensity than no activation. A tag intensity analysis showed that tag(s) of 4,316 of the 4,526 strains tested were represented at least once; 3,706 strains were represented by both tags, 4,072 strains by up-tags only, and 3,950 strains by down-tags only. The results indicate that this microarray will be a powerful analytical platform for elucidating currently unknown gene functions.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42657769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of neoantigens derived from alternative splicing and RNA modification 选择性剪接和RNA修饰新抗原的鉴定

Genomics & informatics Pub Date : 2019-08-22 DOI: 10.5808/GI.2019.17.3.e23

Jiyeon Park, Y. Chung

引用次数: 17

FusionScan: accurate prediction of fusion genes from RNA-Seq data FusionScan:从RNA-Seq数据中准确预测融合基因

Genomics & informatics Pub Date : 2019-07-23 DOI: 10.5808/GI.2019.17.3.e26

P. Kim, Y. Jang, Sanghyuk Lee

{"title":"FusionScan: accurate prediction of fusion genes from RNA-Seq data","authors":"P. Kim, Y. Jang, Sanghyuk Lee","doi":"10.5808/GI.2019.17.3.e26","DOIUrl":"https://doi.org/10.5808/GI.2019.17.3.e26","url":null,"abstract":"Identification of fusion gene is of prominent importance in cancer research field because of their potential as carcinogenic drivers. RNA sequencing (RNA-Seq) data have been the most useful source for identification of fusion transcripts. Although a number of algorithms have been developed thus far, most programs produce too many false-positives, thus making experimental confirmation almost impossible. We still lack a reliable program that achieves high precision with reasonable recall rate. Here, we present FusionScan, a highly optimized tool for predicting fusion transcripts from RNA-Seq data. We specifically search for split reads composed of intact exons at the fusion boundaries. Using 269 known fusion cases as the reference, we have implemented various mapping and filtering strategies to remove false-positives without discarding genuine fusions. In the performance test using three cell line datasets with validated fusion cases (NCI-H660, K562, and MCF-7), FusionScan outperformed other existing programs by a considerable margin, achieving the precision and recall rates of 60% and 79%, respectively. Simulation test also demonstrated that FusionScan recovered most of true positives without producing an overwhelming number of false-positives regardless of sequencing depth and read length. The computation time was comparable to other leading tools. We also provide several curative means to help users investigate the details of fusion candidates easily. We believe that FusionScan would be a reliable, efficient and convenient program for detecting fusion transcripts that meet the requirements in the clinical and experimental community. FusionScan is freely available at http://fusionscan.ewha.ac.kr/.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46412581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Towards cross-platform interoperability for machine-assisted text annotation 面向机器辅助文本注释的跨平台互操作性

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e19

Richard Eckart de Castilho, Nancy Ide, Jin-Dong Kim, Jan-Christoph Klie, Keith Suderman

引用次数: 7

Resources for assigning MeSH IDs to Japanese medical terms 用于将MeSH ID指定为日语医学术语的资源

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e16

Yuka Tateisi

引用次数: 4

Improving spaCy dependency annotation and PoS tagging web service using independent NER services 使用独立的NER服务改进spaCy依赖性注释和PoS标记web服务

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e21

N. Colic, Fabio Rinaldi

引用次数: 8

PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts PharmacoNER Tagger:一个基于深度学习的工具，用于自动在西班牙医学文本中查找化学物质和药物

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e15

Jordi Armengol-Estapé, Felipe Soares, M. Marimon, Martin Krallinger

{"title":"PharmacoNER Tagger: a deep learning-based tool for automatically finding chemicals and drugs in Spanish medical texts","authors":"Jordi Armengol-Estapé, Felipe Soares, M. Marimon, Martin Krallinger","doi":"10.5808/GI.2019.17.2.e15","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e15","url":null,"abstract":"Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43537837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

A review of drug knowledge discovery using BioNLP and tensor or matrix decomposition. 使用BioNLP和张量或矩阵分解的药物知识发现综述。

Genomics & informatics Pub Date : 2019-06-01 Epub Date: 2019-06-27 DOI: 10.5808/GI.2019.17.2.e18

Mina Gachloo, Yuxing Wang, Jingbo Xia

引用次数: 11

Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data 改进CONTES方法，用(几乎)没有训练数据的本体概念规范化生物医学文本实体

Genomics & informatics Pub Date : 2019-06-01 DOI: 10.5808/GI.2019.17.2.e20

Arnaud Ferré, Mouhamadou Ba, Robert Bossy

{"title":"Improving the CONTES method for normalizing biomedical text entities with concepts from an ontology with (almost) no training data","authors":"Arnaud Ferré, Mouhamadou Ba, Robert Bossy","doi":"10.5808/GI.2019.17.2.e20","DOIUrl":"https://doi.org/10.5808/GI.2019.17.2.e20","url":null,"abstract":"Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.","PeriodicalId":94288,"journal":{"name":"Genomics & informatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47317944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5