Sclust paper published on NP

After years fighting, our Sclsut paper published on Nature Protocols finally. Enjoy!

Copy-number analysis and inference of subclonal populations in cancer genomes using Sclust

  • Nature Protocols volume13pages1488–1501 (2018)
  • doi:10.1038/nprot.2018.033
Published: 24 May 2018

Abstract

The genomes of cancer cells constantly change during pathogenesis. This evolutionary process can lead to the emergence of drug-resistant mutations in subclonal populations, which can hinder therapeutic intervention in patients. Data derived from massively parallel sequencing can be used to infer these subclonal populations using tumor-specific point mutations. The accurate determination of copy-number changes and tumor impurity is necessary to reliably infer subclonal populations by mutational clustering. This protocol describes how to use Sclust, a copy-number analysis method with a recently developed mutational clustering approach. In a series of simulations and comparisons with alternative methods, we have previously shown that Sclust accurately determines copy-number states and subclonal populations. Performance tests show that the method is computationally efficient, with copy-number analysis and mutational clustering taking <10 min. Sclust is designed such that even non-experts in computational biology or bioinformatics with basic knowledge of the Linux/Unix command-line syntax should be able to carry out analyses of subclonal populations.

A useful course of biomedical data analysis

Biomedical Data Science: http://genomicsclass.github.io/book/

Chapter 0 – Introduction

Chapter 1 – Inference

Chapter 2 – Exploratory Data Analysis

Chapter 3 – Robust Statistics

Chapter 4 – Matrix Algebra

Chapter 5 – Linear Models

Chapter 6 – Inference for High-Dimensional Data

Chapter 7 – Statistical Modeling

Chapter 8 – Distance and Dimension Reduction

Chapter 9 – Practical Machine Learning

Chapter 10 – Batch Effects


525.5x: Introduction to Bioconductor: Annotation and analysis

Setup and basics on biological background (Week 1)

Focus on data structure and management (Week 2)

Focus on genomic ranges (Week 3a)

Focus on genomic annotation (Week 3b)

Testing genome-scale hypotheses (Week 4)

525.6x: High-performance computing for reproducible genomics with Bioconductor

Visualization of genome scale data (Week 1)

Scalable genomic analysis (Week 2)

Multi-omic data integration (Week 3)

Fostering reproducible genome-scale analysis (Week 4)


Legacy material from 2015 Introduction to Bioconductor

RNA-seq data analysis

Variant Discovery and Genotyping

ChIP-seq data analysis

DNA methylation data analysis


Footnotes for all lectures

Acknowledgments

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“Translational Bioinformatics” collection for PLOS cBio

A review collection in current approach in Translational Bioinformatics.

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COVER
Image Credit: PLOS
Issue Image

‘Translational Bioinformatics’ is a collection of PLOS Computational Biology Education articles which reads as a “book” to be used as a reference or tutorial for a graduate level introductory course on the science of translational bioinformatics.

Translational bioinformatics is an emerging field that addresses the current challenges of integrating increasingly voluminous amounts of molecular and clinical data. Its aim is to provide a better understanding of the molecular basis of disease, which in turn will inform clinical practice and ultimately improve human health.

The concept of a translational bioinformatics introductory book was originally conceived in 2009 by Jake Chen and Maricel Kann. Each chapter was crafted by leading experts who provide a solid introduction to the topics covered, complete with training exercises and answers. The rapid evolution of this field is expected to lead to updates and new chapters that will be incorporated into this collection.

Collection editors: Maricel Kann, Guest Editor, and Fran Lewitter, PLOS Computational Biology Education Editor.

Download the full Translational Bioinformatics collection here: PDF

Collection URL: www.ploscollections.org/translationalbioinformatics

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002796

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002826

Chapter 2: Data-Driven View of Disease Biology

Casey S. Greene, Olga G. Troyanskaya

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002816

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002805

Chapter 4: Protein Interactions and Disease

Mileidy W. Gonzalez, Maricel G. Kann

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002819

Chapter 5: Network Biology Approach to Complex Diseases

Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002820

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002821

Chapter 7: Pharmacogenomics

Konrad J. Karczewski, Roxana Daneshjou, Russ B. Altman

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002817

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002858

Chapter 9: Analyses Using Disease Ontologies

Nigam H. Shah, Tyler Cole, Mark A. Musen

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002827

Chapter 10: Mining Genome-Wide Genetic Markers

Xiang Zhang, Shunping Huang, Zhaojun Zhang, Wei Wang

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002828

Chapter 11: Genome-Wide Association Studies

William S. Bush, Jason H. Moore

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002822

Chapter 12: Human Microbiome Analysis

Xochitl C. Morgan, Curtis Huttenhower

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002808

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002823

Chapter 14: Cancer Genome Analysis

Miguel Vazquez, Victor de la Torre, Alfonso Valencia

PLOS Computational Biology: published 27 Dec 2012 | info:doi/10.1371/journal.pcbi.1002824

 

comutational courese in Plos Computational Biology

Short introduction paper in different ares in computational biology.

Fran Lewitter, Welcome to PLoS Computational Biology “Education”

Social network, machine learning and disease-genes

Some recent paper on how disease gene network works and the metastasis of cancer. Machine  Learning is a good tool for study the relation between individual gene and disease.  here are the papers:

Infectious Disease Modeling of Social Contagion in Networks

Alison L. Hill1,2*, David G. Rand1,3, Martin A. Nowak1,4,5,Nicholas A. Christakis6,7,8

Information, trends, behaviors and even health states may spread between contacts in a social network, similar to disease transmission. However, a major difference is that as well as being spread infectiously, it is possible to acquire this state spontaneously. For example, you can gain knowledge of a particular piece of information either by being told about it, or by discovering it yourself. In this paper we introduce a mathematical modeling framework that allows us to compare the dynamics of these social contagions to traditional infectious diseases. We can also extract and compare the rates of spontaneous versus contagious acquisition of a behavior from longitudinal data and can use this to predict the implications for future prevalence and control strategies. As an example, we study the spread of obesity, and find that the current rate of becoming obese is about 2 per year and increases by 0.5 percentage points for each obese social contact, while the rate of recovering from obesity is 4per year. The rates of spontaneous infection and transmission have steadily increased over time since 1970, driving the increase in obesity prevalence. Our model thus provides a quantitative way to analyze the strength and implications of social contagions.

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A Free Online Book on Human Genome

Recently, I just read a online book on Human Genome. Its an well write book, thanks the author for share. Enjoy the Book:

Human Molecular Genetics, 2nd edition

Tom Strachan1 and Andrew P Read2
1University of Newcastle, Newcastle-upon-Tyne, UK
2University of Manchester, Manchester, UK
New York: Wiley-Liss; 1999.
ISBN: 1-85996-202-5
Excerpt

The idea for this book grew from two earlier efforts, The Human Genome (TS; BIOS Scientific Publishers, 1992) and Medical Genetics, an Illustrated Outline (APR; Gower Medical Publishing, 1989). In these small books we tried to develop a treatment of human genetics based on understanding the structure and function of the normal human genome. Traditionally, textbooks of human genetics tended to start by considering meiosis and the way diseases segregate in pedigrees, whilst textbooks of molecular genetics rarely emphasized human topics. Until recently this was inevitable because so little was understood about the normal human genome. The Human Genome Project has changed all that, and the present book is an attempt to provide a comprehensive integrated study of human molecular genetics.

Table of Contents

Personal genomes

Advances in genome science and technology offer a deeper understanding of biology while at the same time improving the practice of medicine. Genomics information could tell  us more story on our health.In 2008, Nature offered a special focus on “Personal Genomics” to focus on the on how genomes information help us known our health. As the price of sequencing is deducing, someday can say that my gene, my health. Here is a snapshot from Nature.

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