I’m looking at some of the data connected with your recent publication and was wondering if I could get clarification on the BioType attribute in the following file:
In here there appear to be 3 biotypes
Looking through the paper and the supplementary material I can find reference to processed_pseudogene and unprocessed_pseudogene, but not the generic pseudogene? Reading the S1 material I would not expect to see this 3rd biotype
Pseudogenes were classified as “processed” if they have lost their parental gene structures.
Conversely, we classified pseudogenes as “unprocessed”/ “duplicated” if they retained the
same exon-intron structure as their parent loci. In ambiguous cases we used other features to
resolve the provenance of the pseudogene. Where the pseudogene represented a fragment of
the parent, and the homology ended precisely at a splice junction the pseudogene was called
“unprocessed” (“duplicated”). Conversely, where the fragment contained the fusion of two or
more exons the pseudogene was called “processed”. If the parent had a single exon CDS, the
presence of parent gene structure in the 5′ UTR region (identified by alignment of mRNA and
EST evidence) allowed the pseudogene to be called “unprocessed”/“duplicated”. Meanwhile,
the presence of a pseudopoly(A) signal (the position of the parent poly(A) signal at the
pseudogene locus) followed by a tract of A-rich sequence in the genome (indicating the
insertion site of the polyadenylated parental mRNA) indicated a “processed” pseudogene. If
there was no other evidence available to resolve the route by which the pseudogene was
created, we used the position of the pseudogene relative to its parent. As such “processed”
pseudogenes are reinserted into the genome with an approximately random distribution while
“unprocessed”/“duplicated” pseudogenes tend to be more closely associated with the parent
locus. Parsimony therefore suggests that pseudogenes that lie near to the parent locus are
more likely to have arisen via a gene-duplication event than retrotransposition, and this was
used as a tie-breaker in defining the pseudogene biotype.
I hope I haven’t missed anything obvious, but any clarification would help greatly.
When we classify the pseudogenes according to their biotype we have processed pseudogenes and duplicated pseudogenes. This biotype is dependent on the pseudogene formation process (retrotransposition vs duplication) and this is the description that you see in the supplementary material. The third biotype that you find in some of the files on psicube website is actually not a biotype per se, these pseudogenes are most of the time highly degraded or short fragments and we could not assign with high confidence a definite biotype to them. In other words the pseudogenes with “pseudogene” as biotype have actually an undetermined biotype. But instead of saying “NA” (not available or unknown) we opted to simply call them “pseudogene".
I recently read
your 2003 paper titled "Spectral biclustering of microarray data: Coclustering
genes and conditions".
I would like to investigate implementing your approach on a GPU.
Is there any code (Matlab? Python?) you would be willing to share as a result of the paper?
Sorry we’re just using simple SVD routines in matlab. No meaningful code available. -marK
I just read your recently published paper on OrthoClust approach. It is a well grounded work in both practically and mathematically point of views.
I ran your R scripts for my own data and It worked perfectly fine, however I am wondering how can I use the script for more than two species?
It could be appreciated if you help me to find the solution.
Thanks for your interest in OrthoClust. Orthoclust definitely works on more than 2. The R script is a primitive version for illustrating the concept outlined in the paper. We understand the importance of N-species generalization. We have put a new MATLAB code for N-species. It made use of an efficient code written by Mucha and Porter that implemented the Louvain algorithm for modularity optimization. The 3rd party code as well as our wrapper is now in the gersteinlab github.
Apart from MATLAB, we are planning to provide wrapper for Python or R later.
The N-species code is not exactly the thing we did for the paper. So if you find any bug or question, please let me know. we are trying to make a more user friendly package anyway.
I came across your paper ‘The GENCODE pseudogene resource’.
It is a great paper.
Could you please tell me where I would be able to find the list of psuedogenes mentioned in the paper?
I didn’t find any downloadable database in the paper.
see psicube.pseudogene.org & http://pseudogene.org/psidr
I am using your lab database AlleleDB for allelic imbalance SNPs of ChIP-seq data and I really find it a formidable resource for further research.
I noticed that many of the significant SNPs for allelic imbalance are SNPs that have only the reference allele in the ChIP-seq data. See the first 10 lines as an example from your file ASB.auto.v2.1.aug16.txt.tgz that can be downloaded from AlleleDB database :
chr start end TF_indiv_ASB ref alt cA cC cG cT p.binomial p.betabinomial
chr1 91604 91605 SA1_NA19099_ASB C T 0 45 0 0 5.68E-14 0.000376021311925
chr1 714018 714019 POL2_NA18505_ASB A G 2 0 19 0 0.000221252441406 0.003114747121148
chr1 714018 714019 POL2_NA19099_ASB A G 2 0 19 0 0.000221252441406 0.002559182427128
chr1 762484 762485 RPB2_NA11894_ASB C A 0 11 0 0 0.0009765625 0.004566586027278
chr1 762600 762601 RPB2_NA11894_ASB T C 0 12 0 0 0.00048828125 0.002968280917731
chr1 840035 840036 SA1_NA18951_ASB T G 0 0 1 168 2.44E-37 0.001745366786663
chr1 937397 937398 SA1_NA19099_ASB G A 0 0 81 0 8.27E-25 5.03E-05
chr1 1167795 1167796 RPB2_NA11894_ASB C T 0 26 0 0 2.98E-08 2.29E-05
chr1 1407371 1407372 RPB2_NA11831_ASB T C 0 0 0 41 9.09E-13 0.000260638330353
chr1 1551110 1551111 POL2_NA19099_ASB C G 0 30 0 0 1.86E-09 1.67E-06
If I understand well allelic imbalance, only SNPs with genotypes that are heterozygous are tested for allelic imbalance.
In this case, the first SNP in the above table (for instance) has a genotype C/T, but shows 45 reads with C and 0 reads with T. This is indeed a strong allelic imbalance. In your data, there are many examples like that.
However, I heard many biologists saying that cell lines are not stable and mutations accumulate over time.
Is it possible that the first SNP is actually homozygous C/C in the cells used for ChIP-seq experiment because of mutations that occurred during cell culture, given that the genotyping by 1000 Genome project and the ChIP-seq experiment might have been done at different times or by different labs?
What do you think?
Thank you for your interest in AlleleDB!
Right, only heterozygous SNVs are tested for the imbalance. We used the 1000G Project variants for these analyses, did not call them ourselves. Indeed, allelic calculations are very sensitive to genotype calls. Thus, a false-positive heterozygous SNV call, as well as a mutation in the cell-line as you described (not sure if there can be many of those), could lead to a false allelic imbalance call. If this is crucial for your analyses, a simple strategy would probably be filtering the call-set to keep only the SNVs where the ‘weaker’ allele is supported by at least one read, though this would remove some true strong imbalance calls as well.
As describe in your paper entitled "Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors", it is mentioned "we identified 13,539 potential enhancers (full list available in the Additional files), among which 50 were randomly chosen". But in the additional files, only 50 enhancer co-ordinates are mentioned. Can you please provide me either the source/list of the all 13,539 enhancers.
Many thanks in anticipation of your quick reply,
I recently have been working on constructing human regulatory networks. After reading your paper <Genomic analysis of the hierarchical structure of regulatory networks> published on PNAS, I found it very amazing and useful, which may be applied for my study. I want to construct hierarchical structure of transcription factors (TFs) in humans, and my data is the expression level of these TFs and their targets obtained by RNA sequencing. Can we use your BFS method to construct the network? As we know little about the computational algorithm of BFS, would you please provide related scripts or equations for implementing it easily?
Thank you very much for occupying your precious time reading my letter and I’m looking forward to your guidance.
Hi, see http://info.gersteinlab.org/Hierarchy