Protein Bioinformatics Databases and Resources

Chen, Chuming; Huang, Hongzhan; Wu, Cathy H.

doi:10.1007/978-1-60761-977-2_1

Cited by 77 publications

(67 citation statements)

References 84 publications

(14 reference statements)

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“…Variant annotation was done by ANNOVAR (Wang, Li, & Hakonarson, ). The annotated information includes RefSeq gene annotation (O'Leary et al., ), dbSNP rs number (Sherry et al., ), COSMIC (Forbes et al., ), ClinVar (Landrum et al., ), SPIDEX (Xiong et al., ), ExAC conservative constraint (Lek et al., ), UniProt (Chen, Huang, & Wu, ). Background allele frequencies are from SweGen (Ameur et al., ), ExAC (Lek et al., ), gnomAD (Lek et al., ), and 1000 Genomes Project allele frequencies (1000 Genomes Project Consortium et al., ).…”

Section: Methodsmentioning

confidence: 99%

Exome sequencing in 51 early onset non‐familial CRC cases

Thutkawkorapin

Lindblom

Tham

2019

Molec Gen & Gen Med

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Background Colorectal cancer ( CRC ) cases with an age of onset <40 years suggests a germline genetic cause. In total, 51 simplex cases were included to test the hypothesis of CRC as a mendelian trait caused by either heterozygous autosomal dominant or bi‐allelic autosomal recessive pathogenic variants. Methods The cohort was whole exome sequenced ( WES ) at 100× coverage. Both a dominant‐ and recessive model were used for searching predisposing genetic factors. In addition, we assayed recessive variants of potential moderate risk that were enriched in our young‐onset CRC cohort. Variants were filtered using a candidate cancer gene list or by selecting variants more likely to be pathogenic based on variant type (e.g., loss‐of‐function) or allele frequency. Results We identified one pathogenic variant in PTEN in a patient subsequently confirmed to have a hereditary hamartoma tumor syndrome (Cowden syndrome) and one patient with a pathogenic heterozygous variant in PMS 2 that was originally not identified by WES due to low quality reads resulting from pseudogenes. In addition, we identified three heterozygous candidate missense variants in known cancer susceptibility genes ( BMPR 1A , BRIP 1 , and SRC ) , three truncating variants in possibly novel cancer genes ( CLSPN , SEC 24B , SSH 2 ) and four candidate missense variants in ACACA , NR 2C2 , INPP 4A, and DIDO 1 . We also identify five possible autosomal recessive candidate genes: ATP 10B , PKHD 1 , UGGT 2 , MYH 13 , TFF 3 . Conclusion Two clear pathogenic variants were identified in patients that had not been identified clinically. Thus, the chance of detecting a hereditary cancer syndrome in patients with CRC at young age but without family history is 2/51 (4%) and therefore the clinical benefit of genetic testing in this patient group is low. Of note, using stringent filtering, we have identified a total of ten candidate heterozygous variants and five possibly biallelic autosomal recessive candidate genes that warrant further study.

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Section: Methodsmentioning

confidence: 99%

Exome sequencing in 51 early onset non‐familial CRC cases

Thutkawkorapin

Lindblom

Tham

2019

Molec Gen & Gen Med

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“…In just two decades, cost-effective methods have been developed to increase throughput and comprehensive identification of proteins [20,21]; accurately quantify protein relative abundance [22,23]; enrich rare protein types and protein modifications [24, 25]; and expand the capacity and precision of bioinformatic tools that enhance the functional interpretation of proteomic datasets [26,27]. Although some of these methods are still in their infancy, rapid technological and statistical advances daily are increasing the quantity and quality of information about proteins that can be sampled from functioning ecosystems.…”

Section: Understanding the Functional Significance Of Protein Diversitymentioning

confidence: 99%

Environmental proteomics, biodiversity statistics and food-web structure

Gotelli¹,

Ellison²,

Ballif³

2012

Trends in Ecology & Evolution

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Pioneering studies in environmental proteomics have revealed links between protein diversity and ecological function in simple ecological communities such as microbial biofilms. In the near future, high throughput proteomic methods will be applied to more complex ecological systems in which microbes and macrobes interact. Data structures in biodiversity and protein surveys have many similarities, so the statistical methods that ecologists use for analyzing biodiversity data should be adapted for use with quantitative surveys of protein diversity. However, increasing quantities of protein and bioinformatics data will not, by themselves, reveal the functional significance of proteins. Instead, ecologists should be measuring changes in the abundance of protein cohorts in response to replicated field manipulations, including nutrient enrichment and removal of top predators.

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“…As an update to our previously contributed MiMB series chapter [8], we now focus on databases that are aligned with the content of this book and emphasize the types of data stored and related data access and data analysis supports. For each category of databases listed in Table 1, we select some representatives and describe them briefly in section 2.…”

Section: Introductionmentioning

confidence: 99%