BOL: Related items

Gupta Lab

Sat, 29 Dec 2018 13:18:31 -0600

Work include (i) understanding the evolutionary relationships among different prokaryotic and eukaryotic organisms; (ii) Understanding the cellular functions of these lineage-specific signature proteins as well as lineage-specific conserved inserts and deletions in important housekeeping proteins by genetic and biochemical studies; (iii) Development of novel diagnostic methods (PCR based and immunological) for identification of different groups of organisms based upon these signature proteins and conserved indels; (iv) The use of these lineage-specific probes with predicitive ability to identify/explore the presence of different groups of organisms in metagenomic sequences from various environments.

https://fhs.mcmaster.ca/gupta-lab/index.html

TMRCA Calculator

BioStar — Wed, 03 Feb 2021 05:07:30 -0600

This program calculates the probability that two people have a certain number of generations between them, based on the standard infinite alleles formula of Walsh. It calculates both the probability of being at an exact number of generations back to the Most Recent Common Ancestor (MRCA) of a certain pair of people and the cumulative probability that the actual number of generations is less than a certain value. Note that the convention using generations is changed from an earlier version of this calculator which used "transmission events". It can list both result types in a table or graph. In either case the horizontal axis stops at the point where the cumulative probability reaches 95% or 10 generations, whichever is longer, or an absolute max of 50,000. Beyond 90% the calculation becomes inaccurate.

https://clandonaldusa.org/index.php/tmrca-calculator

Address of the bookmark: https://clandonaldusa.org/index.php/tmrca-calculator

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Mon, 28 Feb 2022 23:27:37 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)


Number of extant species	200
Number of extant genes	4303993
Number of ancestral species	196
Number of ancestral genes	4624213
Number of ancestral synteny blocks	83342

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

Genetic basis of tail-loss evolution

LEGE — Tue, 04 Mar 2025 12:12:36 -0600

The paper "On the genetic basis of tail-loss evolution in humans and apes (https://www.nature.com/articles/s41586-024-07095-8)", published in Nature, investigates the genetic mechanisms that led to the loss of tails in humans and apes. The study suggests that a specific genetic mutation, involving the insertion of an Alu element (a type of transposable DNA sequence), played a critical role in the evolutionary transition from tailed primates to tailless hominoids.

Key Findings of the Study:

Alu Insertion and Tail Loss:
The researchers discovered an Alu-mediated genetic change in a common ancestor of modern apes and humans. This change disrupted the normal function of a gene involved in tail development, leading to the suppression of tail formation.
Gene Disruption Mechanism:
The Alu insertion was found within a regulatory region of the TBXT gene (also known as T or Brachyury), which is crucial for tail development in vertebrates. This insertion likely altered the gene's expression patterns, leading to tail reduction over evolutionary time.
Functional Evidence from Model Organisms:
To test their hypothesis, the researchers introduced similar genetic modifications in mice. The modified mice exhibited shortened or absent tails, supporting the idea that the identified mutation played a role in tail loss in hominoids.
Evolutionary Implications:
The findings suggest that small, random genomic changes—such as transposable element insertions—can have profound effects on body morphology. This study provides evidence that mobile DNA elements (like Alu) can drive major evolutionary transitions.
Relevance to Human Evolution:
Understanding the genetic basis of tail loss helps in reconstructing the evolutionary history of hominins (the lineage that includes humans and our extinct relatives). It also sheds light on how genetic variations contribute to anatomical diversity among primates.

Significance of the Study:

This research highlights the role of transposable elements in shaping evolutionary traits and provides a concrete genetic explanation for a defining characteristic of humans and great apes. It also demonstrates how mutations in regulatory regions of developmental genes can lead to significant anatomical changes.

SYSTEMS BIOLOGY APPROACH TO MODEL INFLAMMATION IN HUMAN (2).pptx

ROSHNI — Thu, 17 Oct 2013 00:54:13 -0500

Reference Sequence Resource!

LEGE — Wed, 15 Sep 2021 21:15:22 -0500

The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. T

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

Public Databases for Bioinformatics !

Jit — Tue, 23 Mar 2021 05:32:15 -0500

https://www.nature.com/articles/s41467-020-17155-y

Server Infrastructure:

File Server:

dhara: Synology 3614 Storage Appliance
4 Core Xeon
108TB disk storage
10Gb ethernet to SCG3
Access atx: dhara:5000
Has btsync server (try it - its much better than dropbox)

Compute Servers:

nandi: Kundaje and Phi Server
24 intel cores
256GB RAM
500GB of SSD storage 
36TB RAID6 local storage
4 Intel Phi's (space for 4 more GPU's)


durga: Montgomery and sensitive data
24 intel cores
256GB RAM
500GB of SSD RAID0 storage 
60TB RAID6 local storage

mitra: Bassik and Web/DB Server
24 core
256GB RAM 
500GB of SSD RAID0 storage 
36TB RAID6 local storage

vayu: Kundaje GPU server
4 core
64GB RAM 
200GB of SSD storage 
8TB RAID10 local storage
4 Nvidia GTX 970 4GB GPUs

amold: Bickel and SGE server
32 AMD core
128GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

wotan: Bickel and SGE server
64 AMD core
256GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

Filesystem:

/users/$USER
default home directory
full backups nightly 
nfs mount to dhara
should store code, papers, and other highly processed data here

/mnt/data/
globally accessible data
should store common data here
e.g. genomes and indexes, annotations, ENCODE data  
if you dont want this to count towards your quote you must chown

/mnt/lab_data/$LAB/
lab accessible data
should store lab project data here 
e.g. ATAC-seq prediction data, enhancer prediction, motif calls

/srv/scratch/$USER
fast local storage
not backed up, but on raid and data will never be deleted
most analysis should be performed here

/srv/persistent/$USER
fast local storage
synced nightly, but not backed up
       ie if the hard drives fail or you delete something and notice 
       within 24 hours we can recover. Otherwise not. (vs home which is 
       properly backed up )  
intermediate analysis products that would be hard to recover should be stored here 
       e.g. stochastic analysis results that need to be kept so that paper 
       results can be reproduced

/srv/www/$LABNAME/
web accessible from mitra.stanford.edu
*NOT BACKED UP*

Some parallel programming patterns:

# gzip a bunch of files
parallel gzip -- *.FILESTOGZIP

# fork example in python:
(for more detailed examples look at 
 https://github.com/nboley/grit/ grit/lib/multiprocessing_utils.py)

import os
import time
import random

import multiprocessing

class ProcessSafeOPStream( object ):
    def __init__( self, writeable_obj ):
        self.writeable_obj = writeable_obj
        self.lock = multiprocessing.Lock()
        self.name = self.writeable_obj.name
        return
    
    def write( self, data ):
        self.lock.acquire()
        self.writeable_obj.write( data )
        self.writeable_obj.flush()
        self.lock.release()
        return
    
    def close( self ):
        self.writeable_obj.close()

def worker(queue, ofp):
    # Try without this
    random.seed()
    while True:
        i = queue.get()
        if i == 'FINISHED': return
        # simulate an expensive function
        x = random.random()
        time.sleep(x/10)
        print i, x
        ofp.write("%i\t%s\n" % (i, x))

NSIMS = 10000
NPROC = 25

# populate queue
todo = multiprocessing.Queue()
for i in xrange(NSIMS): todo.put(i)
for i in xrange(NPROC): todo.put('FINISHED')

ofp = ProcessSafeOPStream( open("output.txt", "w") )

pids = []
for i in xrange(NPROC):
    pid = os.fork()
    if pid == 0:
       worker(todo, ofp)
       os._exit(0)
    else:
       pids.append(pid)  

for pid in pids:
    os.waitpid(pid, 0)

ofp.close()

print "FINISHED"

For use case 1 we obtained the following ENCODE and ROADMAP datasets https://www.encodeproject.org/files/ENCFF446WOD/@@download/ENCFF446WOD.bed.gz, https://www.encodeproject.org/files/ENCFF546PJU/@@download/ENCFF546PJU.bam, https://www.encodeproject.org/files/ENCFF059BEU/@@download/ENCFF059BEU.bam. Blacklisted regions were obtained from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz. The human genome version hg38 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz.

For use case 2 we used the set of narrowPeak files summarized in https://github.com/wkopp/janggu_usecases/tree/master/extra/urls.txt (archived version v1.0.1). The human genome version hg19 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz

For use case 3 we used the ENCODE datasets https://www.encodeproject.org/files/ENCFF591XCX/@@download/ENCFF591XCX.bam, https://www.encodeproject.org/files/ENCFF736LHE/@@download/ENCFF736LHE.bigWig, https://www.encodeproject.org/files/ENCFF177HHM/@@download/ENCFF177HHM.bam as we as the GENCODE annotation v29 from ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz.

Address of the bookmark: http://mitra.stanford.edu/

chromoMap-An R package for Interactive visualization and mapping of human chromosomes

Rahul Nayak — Mon, 25 Jun 2018 17:22:24 -0500

chromoMap is an R package that provides interactive, configurable and elegant graphics visualization of the human chromosomes allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. Because of the enormous size of the chromosomes, it is impractical to visualize each element on the same plot. But chromoMap plots provide a magnified view for each of chromosome location to render additional information and visualization specific for that location. You can map thousands of genes and can view all mappings easily. Users can investigate the detailed information about the mappings (like gene names or total genes mapped on a location) or can view the magnified single or double stranded view of the chromosome at a location showing each mapped element in sequential order (You will see in the demos below). Not ony that, the plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

https://cran.r-project.org/web/packages/chromoMap/index.html

PLAR: Pipeline for lncRNA annotation from RNA-seq data

Abhi — Fri, 07 Jan 2022 06:18:01 -0600

Due to several requests, we are releasing an assingment of orthologs, determined using the same methods used in Hezroni et al. (BLAST, Whole Genome Alignment (WGA), and synteny). One is comparing human GENCODE genes (from GENCODE v30) to lncRNAs from other species identified by PLAR. Available here.

Species	Assembly	Code	Transcriptome	lncRNAs	Protein-coding
Human	hg19	hg19	Download	Download	Download
Rhesus	rheMac3	rm3	Download	Download	Download
Marmoset	calJac3	cj3	Download	Download	Download
Mouse	mm9	mm9	Download	Download	Download
Rabbit	oryCun2	oc2	Download	Download	Download
Dog	canFam3	cf3	Download	Download	Download
Ferret	musFur1	oa3	Download	Download	Download
Opossum	monDom5	md5	Download	Download	Download
Chicken	galGal4	gg4	Download	Download	Download
Lizard	anoCar2	ac2	Download	Download	Download
Coelacanth	latCha1	lc1	Download	Download	Download
Zebrafish	danRer7	dr7	Download	Download	Download
Stickleback	gasAcu1	ga1	Download	Download	Download
Nile tilapia	oreNil2	ot2	Download	Download	Download
Spotted gar	lepOcu1	lo1	Download	Download	Download
Elephant shark	calMil1	cm1	Download	Download	Download
Sea urchin	strPur4	sp4	Download	Download	Download

Address of the bookmark: http://www.weizmann.ac.il/Biological_Regulation/IgorUlitsky/PLAR

Claus-Peter Stelzer Lab

Mon, 15 Mar 2021 15:24:41 -0500

Interested in various topics at the intersection of ecology and evolution. In my research I use rotifers as model organisms for experimental studies at the individual and population level. Rotifers are ideally suited for this, because populations of thousands can be kept in small containers in the lab, while single individuals can still be handled conveniently.

More at https://www.uibk.ac.at/limno/personnel/stelzer/index.html.en#research