scholarly journals Using Tablet for visual exploration of second-generation sequencing data

2012 ◽  
Vol 14 (2) ◽  
pp. 193-202 ◽  
Author(s):  
I. Milne ◽  
G. Stephen ◽  
M. Bayer ◽  
P. J. A. Cock ◽  
L. Pritchard ◽  
...  
Keyword(s):  
Second Generation ◽  
Visual Exploration ◽  
Sequencing Data ◽  
Second Generation Sequencing ◽  
Generation Sequencing

scholarly journals A case report of the differential diagnosis of Cellulosimicrobium cellulans-infected endocarditis combined with intracranial infection by conventional blood culture and second-generation sequencing

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Huifang Zhang ◽  
Chunyan He ◽  
Rui Tian ◽  
Ruilan Wang
Keyword(s):  
Second Generation ◽  
Human Infection ◽  
Sequencing Data ◽  
Case Presentation ◽  
Intracranial Infection ◽  
Cellulosimicrobium Cellulans ◽  
Two Samples ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Abstract Background Cellulosimicrobium cellulans is a gram-positive filamentous bacterium found primarily in soil and sewage that rarely causes human infection, especially in previously healthy adults, but when it does, it often indicates a poor prognosis. Case presentation We report a case of endocarditis and intracranial infection caused by C. cellulans in a 52-year-old woman with normal immune function and no implants in vivo. The patient started with a febrile headache that progressed to impaired consciousness after 20 days, and she finally died after treatment with vancomycin combined with rifampicin. C. cellulans was isolated from her blood cultures for 3 consecutive days after her admission; however, there was only evidence of C. cellulans sequences for two samples in the second-generation sequencing data generated from her peripheral blood, which were ignored by the technicians. No C. cellulans bands were detected in her cerebrospinal fluid by second-generation sequencing. Conclusions Second-generation sequencing seems to have limitations for certain specific strains of bacteria.

scholarly journals CNAseg—a novel framework for identification of copy number changes in cancer from second-generation sequencing data

2010 ◽  
Vol 26 (24) ◽  
pp. 3051-3058 ◽  
Author(s):  
Sergii Ivakhno ◽  
Tom Royce ◽  
Anthony J. Cox ◽  
Dirk J. Evers ◽  
R. Keira Cheetham ◽  
...  
Keyword(s):  
Copy Number ◽  
Second Generation ◽  
Sequencing Data ◽  
Second Generation Sequencing ◽  
Copy Number Changes ◽  
Generation Sequencing

Mining microsatellites for Gray’s beaked whale from second-generation sequencing data

2014 ◽  
Vol 6 (3) ◽  
pp. 657-659 ◽  
Author(s):  
Selina Patel ◽  
Kirsten Thompson ◽  
Liam Williams ◽  
Peter Tsai ◽  
Rochelle Constantine ◽  
...  
Keyword(s):  
Second Generation ◽  
Sequencing Data ◽  
Beaked Whale ◽  
Second Generation Sequencing ◽  
Generation Sequencing

STRait Razor: A length-based forensic STR allele-calling tool for use with second generation sequencing data

2013 ◽  
Vol 7 (4) ◽  
pp. 409-417 ◽  
Author(s):  
David H. Warshauer ◽  
David Lin ◽  
Kumar Hari ◽  
Ravi Jain ◽  
Carey Davis ◽  
...  
Keyword(s):  
Second Generation ◽  
Sequencing Data ◽  
Second Generation Sequencing ◽  
Generation Sequencing ◽  
Allele Calling

scholarly journals Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data

2015 ◽  
Author(s):  
Mikhail Lipatov ◽  
Komal Sanjeev ◽  
Rob Patro ◽  
Krishna Veeramah
Keyword(s):  
Second Generation ◽  
Sequence Data ◽  
Likelihood Estimation ◽  
Allele Frequencies ◽  
Human Populations ◽  
Sequencing Data ◽  
Dna Sequence Data ◽  
Second Generation Sequencing ◽  
Low Coverage ◽  
Generation Sequencing

The inference of biological relatedness from DNA sequence data has a wide array of applications, such as in the study of human disease, anthropology and ecology. One of the most common analytical frameworks for performing this inference is to genotype individuals for large numbers of independent genomewide markers and use population allele frequencies to infer the probability of identity-by-descent (IBD) given observed genotypes. Current implementations of this class of methods assume genotypes are known without error. However, with the advent of second generation sequencing data there are now an increasing number of situations where the confidence attached to a particular genotype may be poor because of low coverage. Such scenarios may lead to biased estimates of the kinship coefficient, Φ. We describe an approach that utilizes genotype likelihoods rather than a single observed best genotype to estimate Φ and demonstrate that we can accurately infer relatedness in both simulated and real second generation sequencing data from a wide variety of human populations down to at least the third degree when coverage is as low as 2x for both individuals, while other commonly used methods such as PLINK exhibit large biases in such situations. In addition the method appears to be robust when the assumed population allele frequencies are diverged from the true frequencies for realistic levels of genetic drift. This approach has been implemented in the C++ software lcmlkin.

scholarly journals Precision Medication of Tacrolimus Based on the Genotype by Using Second-generation Sequencing

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Yumeng Li
Keyword(s):  
Organ Transplantation ◽  
Second Generation ◽  
Low Cost ◽  
Single Nucleotide ◽  
Factors Affecting ◽  
Key Enzyme ◽  
Second Generation Sequencing ◽  
Degree Level ◽  
Generation Sequencing ◽  
Related Mortality

Organ transplantation has become a powerful strategy for the treatment of malignant diseases. Nevertheless, graft rejection is one of the main factors affecting graft survival after organ transplantation. Under this circumstance, the transplant-related mortality still keeps up. This invention includes the precise medication guidance of Tacrolimus (FK506) inapplicable population, against the side-effects of this drug. This invention, based on second-generation sequencing, has the advantages of relatively low cost and high sequencing throughput. During the design process, we collect the data of single nucleotide polymorphism (SNP) concerning the adverse drug reactions of Tacrolimus. Then we filter and summarize fifteen SNPs basing on importance degree (level >key enzyme>race). Thenceforth, after the process of analyzing the raw extract by operating BWA, Picard-tools, GATK, and Perl, we annotate SNPs by Annovar. Through this innovation, people can obtain further feedback on drugs that targets different genes in order to achieve the purpose of precision medication and minimizing the risks of misusing Tacrolimus.

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