Tree Reconciliation Study

0
Bayesian gene/species tree reconciliation and orthology analysis using MCMC
Bioinformatics (2003) 19 (suppl 1): i7-i15.
http://bioinformatics.oxfordjournals.org/content/19/suppl_1/i7.abstract

1
HGTree: database of horizontally transferred genes determined by tree reconciliation
Nucl. Acids Res. (04 January 2016) 44 (D1): D610-D619.
http://nar.oxfordjournals.org/content/44/D1/D610.full
use of Ranger-DTL

2
Estimating Gene Gain and Loss Rates in the Presence of Error in Genome Assembly and Annotation Using CAFE 3
Mol Biol Evol (2013) 30 (8): 1987-1997.
http://mbe.oxfordjournals.org/content/30/8/1987.full

CAFE3 
Download: http://www.indiana.edu/~hahnlab/software.html
Manual: http://www.indiana.edu/~hahnlab//Programs/CAFE3.0/CAFE_3.0_Manual_Aug1Update.pdf

3
BadiRate: estimating family turnover rates by likelihood-based methods
Bioinformatics (2012) 28 (2): 279-281.
http://bioinformatics.oxfordjournals.org/content/28/2/279.full

BadiRate
Download: http://www.ub.edu/softevol/badirate/
Manual: http://www2.ub.es/softevol/badirate/BadiRateManual.pdf

4 
The Extent of Genome Flux and Its Role in the Differentiation of Bacterial Lineages
Genome Biol Evol (2014) 6 (6): 1514-1529.
http://gbe.oxfordjournals.org/content/6/6/1514.full
use of GLOOME

5
Genome-scale phylogenetic analysis finds extensive gene transfer among fungi
PhilTransRSocB September 2015 Volume: 370 Issue: 1678
http://rstb.royalsocietypublishing.org/content/370/1678/20140335
use of Count and ALE

6 
Gene Loss Dominates As a Source of Genetic Variation within Clonal Pathogenic Bacterial Species
Genome Biol Evol (2015) 7 (8): 2173-2187.
http://gbe.oxfordjournals.org/content/7/8/2173.full
correction for absence of protein owing to misannotations and pseudogenization before inferring gain/loss/HGT using TFASTX (like TBLASTX)
gene gain was considered to be equivalent with HGT event

7
SylvX viewer for phylogenetic reconciliation
http://www.sylvx.org/

8
The Inference of Gene Trees with Species Trees
Syst Biol (2015) 64 (1): e42-e62.
http://sysbio.oxfordjournals.org/content/64/1/e42.full
reviewing the gene tree reconciliation problems

9
Gitools: Analysis and Visualisation of Genomic Data Using Interactive Heat-Maps
PLoS ONE 6(5): e19541. 
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019541

Gitools
Download: http://www.gitools.org/
Manual: http://www.gitools.org/documentation/

10
Reconciliation Approaches to Determining HGT, Duplications, and Losses in Gene Trees
Methods in Microbiology Volume 41, 2014, Pages 183?199
http://www.sciencedirect.com/science/article/pii/S0580951714000166
understanding different reconciliation methods 
practical steps of using AnGST 
practical steps of using Count 

11
Determining the evolutionary history of gene families
Bioinformatics (2012) 28 (1): 48-55
http://bioinformatics.oxfordjournals.org/content/28/1/48.full
gain,loss

DupliPHY and DupliPHY-ML
http://www.bioinf.manchester.ac.uk/dupliphy/
Download: http://www.bioinf.manchester.ac.uk/lovell/dupliphy.html
Manual: http://www.bioinf.manchester.ac.uk/dupliphy/help.html
Step by step guide: http://www.bioinf.manchester.ac.uk/dupliphy/walkthrough.html

12
Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood
Bioinformatics (2010) 26 (15): 1910-1912.
http://bioinformatics.oxfordjournals.org/content/26/15/1910.full

Count
Download: http://www.iro.umontreal.ca/~csuros/gene_content/count.html
Manual: http://www.iro.umontreal.ca/~csuros/gene_content/count-usage.pdf

13
Extinction probabilities and stationary distributions of mobile genetic elements in prokaryotes: The birth?death-diversification model
Theoretical Population Biology Volume 106, December 2015, Pages 22?31
http://www.sciencedirect.com/science/article/pii/S0040580915000866
understanding Birth-Death-Diversification model of gene family evolution

14
Models, algorithms and programs for phylogeny reconciliation
Brief Bioinform (2011) 12 (5): 392-400.
http://bib.oxfordjournals.org/content/12/5/392.full

2014, PNAS, Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

Global genomic and transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism

http://www.pnas.org/content/early/2014/09/04/1402665111.abstract


Genetic variation can modulate gene expression, and thereby phenotypic variation and susceptibility to complex diseases such as type 2 diabetes (T2D). Here we harnessed the potential of DNA and RNA sequencing in human pancreatic islets from 89 deceased donors to identify genes of potential importance in the pathogenesis of T2D. We present a catalog of genetic variants regulating gene expression (eQTL) and exon use (sQTL), including many long noncoding RNAs, which are enriched in known T2D-associated loci. Of 35 eQTL genes, whose expression differed between normoglycemic and hyperglycemic individuals, siRNA of tetraspanin 33 (TSPAN33), 5′-nucleotidase, ecto (NT5E), transmembrane emp24 protein transport domain containing 6 (TMED6), and p21 protein activated kinase 7 (PAK7) in INS1 cells resulted in reduced glucose-stimulated insulin secretion. In addition, we provide a genome-wide catalog of allelic expression imbalance, which is also enriched in known T2D-associated loci. Notably, allelic imbalance in paternally expressed gene 3 (PEG3) was associated with its promoter methylation and T2D status. Finally, RNA editing events were less common in islets than previously suggested in other tissues. Taken together, this study provides new insights into the complexity of gene regulation in human pancreatic islets and better understanding of how genetic variation can influence glucose metabolism.


An example of comprehensive analysis on the combined genomic (SNP array, exome) and transcriptomic (microarray, RNA-seq) data.  

Comparative analysis of regulatory information and circuits across distant species

Comparative analysis of regulatory information and circuits across distant species

Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology^1, 2. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.

http://www.nature.com/nature/journal/v512/n7515/full/nature13668.html

2014. PNAS. Adaptive, convergent origins of the pygmy phenotype in African rainforest hunter-gatherers.

Article URL
http://www.pnas.org/content/111/35/E3596.full?sid=a40e2ce9-7a55-4db4-b9ea-932241ba3942

Adaptive, convergent origins of the pygmy phenotype in African rainforest hunter-gatherers

1. George H. Perrya,1,2, 
2. Matthieu Follb,c,1, 
3. Jean-Christophe Grenierd, 
4. Etienne Patine,f, 
5. Yohann Nédélecd,g,
6. Alain Pacisd,g, 
7. Maxime Barakatth, 
8. Simon Graveli, 
9. Xiang Zhouj, 
10. Sam L. Nsobyak, 
11. Laurent Excoffierc,l,
12. Lluis Quintana-Murcie,f, 
13. Nathaniel J. Dominym, and 
14. Luis B. Barreirod,n,1,2

Author Affiliations

1. aDepartments of Anthropology and Biology, Pennsylvania State University, University Park, PA 16802;
2. bSchool of Life Sciences, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;
3. cSwiss Institute of Bioinformatics, 1015 Lausanne, Switzerland;
4. dSainte-Justine Hospital Research Centre, Montreal, QC, Canada H3T 1C5;
5. eInstitut Pasteur, Unit of Human Evolutionary Genetics, 75015 Paris, France;
6. fCentre National de la Recherche Scientifique, URA3012, 75015 Paris, France;
7. gDepartment of Biochemistry, Faculty of Medicine, University of Montreal, Montreal, QC, Canada H3T 1C5;
8. hSchool of Computer Science, and
9. iDepartment of Human Genetics, McGill University, Montreal, QC, Canada H3A 1B1;
10. jDepartments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637;
11. kDepartment of Pathology, Makerere University, Kampala, Uganda;
12. lInstitute of Ecology and Evolution, University of Bern, 3012 Bern, Switzerland;
13. mDepartments of Anthropology and Biological Sciences, Dartmouth College, Hanover, NH 03755; and
14. nDepartment of Pediatrics, Faculty of Medicine, University of Montreal, Montreal, QC, Canada H3T 1C5

The evolutionary history of the human pygmy phenotype (small body size), a characteristic of African and Southeast Asian rainforest hunter-gatherers, is largely unknown. Here we use a genome-wide admixture mapping analysis to identify 16 genomic regions that are significantly associated with the pygmy phenotype in the Batwa, a rainforest hunter-gatherer population from Uganda (east central Africa). The identified genomic regions have multiple attributes that provide supporting evidence of genuine association with the pygmy phenotype, including enrichments for SNPs previously associated with stature variation in Europeans and for genes with growth hormone receptor and regulation functions. To test adaptive evolutionary hypotheses, we computed the haplotype-based integrated haplotype score (iHS) statistic and the level of population differentiation (FST) between the Batwa and their agricultural neighbors, the Bakiga, for each genomic SNP. Both |iHS| and FST values were significantly higher for SNPs within the Batwa pygmy phenotype-associated regions than the remainder of the genome, a signature of polygenic adaptation. In contrast, when we expanded our analysis to include Baka rainforest hunter-gatherers from Cameroon and Gabon (west central Africa) and Nzebi and Nzime neighboring agriculturalists, we did not observe elevated |iHS| or FST values in these genomic regions. Together, these results suggest adaptive and at least partially convergent origins of the pygmy phenotype even within Africa, supporting the hypothesis that small body size confers a selective advantage for tropical rainforest hunter-gatherers but raising questions about the antiquity of this behavior.

열대우림에 살아온 부족 몇 몇이 피그미 형질(작은 몸집)을 가지게 된 진화적 배경에 대한 기존의 가설에서 Adaptive evolution이라는 부분도 테스트하고 Convergent evolution이라는 부분도 테스트한다.

데이터 자체는 genome wide SNP data이다.

피그미 population과 비교를 위해 Control로 인근 지역에 사는 농경 population에서도 SNP을 typing했다.

분석 툴은 ADMIXTURE로 population structure와 individual별 ancestry proportion을 구했고

HAPMIX로 SNP별 ancestry proportion을 구했다.

Genomic region을 나누어 각 region별로   키 vs proportion of 농경population's ancestry 를  구하여

피그미 형질과 연관된 genomic region을 뽑아냈다.

피그미 형질과 연관된 것으로 뽑아낸 genomic region에서 signature of selection을 scan하였다.

이것이 피그미 형질에 대한 population 수준에서의 genome-wide association 연구이지만 

다른 어떤 중요한 형질에 대해서도 population 수준에서 특이점이 있을 때 (예를 들어 멕시코인이 술을 잘 마신다)   비슷한 방법으로 술 잘 마시는 것과 연관된 genomic region을 찾고,  술 잘 마시는 형질이 adaptive evolution인지 테스트할 수 있을 것이다.

마찬가지로 러시아 인도 술을 잘 마시고 멕시코 인도 술을 잘 마시는데 그것이 convergent evolution인지 테스트할 때에도 비슷한 데이터 디자인과 분석 툴을 사용하면 될 것이다.

2013, Cell, Diverse Mechanisms of Somatic Structural Variations in Human Cancer Genomes

Diverse Mechanisms of Somatic Structural Variations in Human Cancer Genomes

• Lixing Yang¹, 
• Lovelace J. Luquette¹, 
• Nils Gehlenborg^1, 3, 
• Ruibin Xi^1, 4, 
• Psalm S. Haseley^1, 5, 
• Chih-Heng Hsieh⁶, 
• Chengsheng Zhang⁶, 
• Xiaojia Ren⁵, 
• Alexei Protopopov⁷, 
• Lynda Chin⁷, 
• Raju Kucherlapati^2, 5,
• Charles Lee^6, 8, 
• Peter J. Park^1, 5, 9,

Summary

Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-throughput sequencing data. However, characterization of complex structural alterations and their underlying mechanisms remains inadequate. Here, applying an algorithm to predict structural variations from short reads, we report a comprehensive catalog of somatic structural variations and the mechanisms generating them, using high-coverage whole-genome sequencing data from 140 patients across ten tumor types. We characterize the relative contributions of different types of rearrangements and their mutational mechanisms, find that ∼20% of the somatic deletions are complex deletions formed by replication errors, and describe the differences between the mutational mechanisms in somatic and germline alterations. Importantly, we provide detailed reconstructions of the events responsible for loss of CDKN2A/B and gain of EGFR in glioblastoma, revealing that these alterations can result from multiple mechanisms even in a single genome and that both DNA double-strand breaks and replication errors drive somatic rearrangements.

http://www.sciencedirect.com/science/article/pii/S0092867413004510

140 tumor-normal pairs , whole genome sequencing,

SV detection & mechanisms indetification,
impressive SV  case study