Computational Genomics - Science topic

Use PHASTER, an online tool for the detection of phage sequences in your genome. it will give you specific files with the regions then you can blast those specific phage sequences in NCBI and can do further analysis.

Muhammad Arslan This is like 4 years late but maybe it gets found by someone in the future and can serve as a record. I tried the tool on all three OSs and the same problem persisted. However, it is not that chmod +x does not work, it is that after it is run it is really hard for the terminal to see the file and run it. In all three cases, this is overcome by adding the path to the file in front of its name as you run it. So, for example, instead of running

You would add in the path and run

Easiest way to know wha the exact path is is to just drag and drop the file into the terminal window and the terminal will tell you

Furthermore, to be more explicit, the exact steps to get this tool to run are:

1) Download it, unzip it (on Linux, or Ubuntu on PC or equivalent) you run "unzip NameOfFile" and you may have to install unzip first via "sudo apt-get install unzip"

2) Run chmod +x on the unzipped file. I did confirm that at this stage, you can rename the file to whatever you want, such as instead of "linux64.table2asn" you can just name it table2asn (I think the full name will be table2asn.table2asn but you can call it up with just table2asn)

3) Ru it with the path, or if you know what you are doing, you can add the file to the path and then it will run with just "table2asn" instead of needing to include the path. However, and this was so confusing to me, even if your terminal is in the same directory that the table2asn file is in it will still not see it and call it up, so I am not 100% sure if the "adding to path" route would fix the issue; I didn't try it yet

Anyways, for anyone searching the web for how to get this to work, there's the steps. A more comprehensive README if you will. As for all the options and tools, I haven't actually run this successfully yet (need to specify locus-tags apparently) so I cannot comment about the options yet

As far as I know, you can find uncorrected p-distances in Geneious once you have your alignment.

Hi!

I wonder if someone knows how reliable WebMGA is. I would like to know your opinion

I suppose a case could be argued both for and against anonimisation, not only in the value to the individual, but also in its actual efficiency to protect individual privacy. At the end of the day, our DNA information itself is a FAR more precise identifyer than any other data that is classed as "identifyer". Hence, the need for genomic privacy is more acute than ever...

May you share your script with me, Daniel M Fernandes?

Thank you very much!

I know this is an old post, but for new people with the same question you may find the below information from Ensembl useful:

If you just want the most biologically relevant transcript

If a human gene has a MANE Select transcript, this is the one you should use. This is an agreement between NCBI and Ensembl as to the clinically most relevant transcript, and the transcript structure matches perfectly between the two databases.

If a gene does not have a MANE Select transcript, APPRIS, provides a similar evaluation of the most biologically relevant transcript, which they call the Principal Isoform. They most likely candidate will be the P1 but they can score down to P5.

If those are not available, you may wish to consider the quality of annotation. A CCDS identifier indicates that the coding region of the transcript is matched between NCBI and Ensembl, while a gold transcript indicates matching annotation by the two different methods of annotation in Ensembl. The TSL gives a score for the amount of data that supports the existence of a transcript.

Does anyone have any idea what Bionumerics software cost? I saw on one website they mentioned something like $7,000 at least?!

It is chrom.size file format not matching bedgraph file.

for Inhouse analysed data.

If bed files downloaded from Publicly available databases

1. Check Chrom.size file format (i.e UCSC, Ensembl or Gencode),

2.) then "cat" your bed or bedgraph file to find confirm structure of bed file rownames i.e written as chr1 or 1 chr_GL456210.1 or just GL456210.1

3.) Adapt the chrom.sizes file according to bed file e.g UCSC chrom.size file contains - chr number_GL456210v1_random in place of GL456210.1

4.)Adapt it similar to your bed files e.g remove prefix chr and chromosome number

Dr. Konopka:

Ultimately, nullomers are short sequences of nucleotides that are not represented within a DNA molecule. For a long sequence drawn on a small alphabet, such as DNA, there are many, many short sub-sequences that are not represented; I will forward to you in a subsequent message an analysis of the shustrings and nullomers of the RAND million random digits.

I am especially interested in particularly short nullomers; why are they not conserved in the genetic record. Please consider this graph, derived from shustring analysis of the Vibreo cholera genome.

wrb

Anny Camelo I did not actually get what you wanted to ask. If you wanted to do that for your data for already assigned OTUs, unfortunately you cant. You can remove the low abundant OTU but I cant say whether it would be because of biology or because of index mis-assignment. It can however be fixed at an earlier level during the library preparation by using dual indexes.

You can also refer OrthoDB 9.1 (https://www.orthodb.org/) for unique ortholog genes.

Hello, I actaully had this same issue in the samtools manual it states:

"At a position, read maximally INT reads per input file. Note that samtools has a minimum value of 8000/n where nis the number of input files given to mpileup. This means the default is highly likely to be increased. Once above the cross-sample minimum of 8000 the -d parameter will have an effect. [250]"

However I am still unsure and am wondering if I should omit -d altogether for mpileup. Where you able to get this sorted?

Hi,

From your question, I understand that you might not be very comfortable with linux and command line based tools. I would suggest you to visit wAnnovar (http://wannovar.wglab.org/). Make sure you provide same genome build and gene definition as used for NGS analysis. Another good option is Variant Effect Predictor (https://asia.ensembl.org/info/docs/tools/vep/index.html). Both are very comprehensive variant annotation tools.

I hope this suggestion helps you with your analysis.

You can use Alienness:

Publication available here:

It works for HGT or any origin to any destination.

If you need help, with this tool, do not hesitate to ask.

Hi annalisa

bed files are just tabulated files, you can also open them with excel if you want....

if I can help, just tell me more.

fred

Thanks Scott... I'll try

Thank you very much for all the advices Mat. They have been really useful!!!

Best regards,

Andrea

Just to complement Brian's answer a little, I would say that, even for amino acid sequences, 6-20% of identity is an extremely low value for homologues, so please specify to what gene or protein sequence you were referring to with that range. If you were referring to whole genome alignment then the sequences were definitively not well aligned. I think you might want to look for a phylogenetic marker gene for your virus family instead. Also, please remember that when one deals with sequences one can only speaks of identities or similarities. Homology (or lack of) can then be inferred from there.. 

don't know if this fits your search, but it takes in consideration both spatial and sequence features

Hi, you can use a big set of tools for Functional Enrihment analysis.

EnrichR : web tool with a lot data bases (GO, KEGG.. etc)

GeneCodis: web tool

Gorila

You can also perform an enrichment analysis using programs as GSEA (in java, with graphical interface). Easy to use and flexible, this program not apply the convectional statistic methods to perform enrichment ( as fisher, hypergeometric test..) and you can get interesting results.

You can also use R programming language, with packages as TopGO, GOstats.. among others..

I hope help you.

best regards

Why not? as long as it shows a high identity percentage solely with such a record.

True Joan..  did not know CD-HIT can be used to cluster nucleotide sequences too, which is why I did not recommended that tool (I have used that tool to cluster protein sequences only). However, I've checked the associated website and it seems that project isn't active anymore. Also, some problems in the CD-HIT algorithm have been reported (actually in the very same USEARCH article). But the problem with USEARCH is that only the 32bit version is free and that limits the number of sequences you can cluster. For Raman's question there is no problem since he is going to cluster at most hundreds of sequences, but when you have millions, then 32bits version of USEARCH cannot be used. There is, however, a project called VSEARCH (https://github.com/torognes/vsearch) which aims to be a free and unrestricted version of USEARCH.

Just saying this in case you might want to check it, Joan.. 

Best regards.

Easiest solution is to not use paired-end reads. This is not necessary for pure counts-based RNAseq (as opposed to differential splicing etc.)

Hi Debjoyti, 

If you want a quick and easy access to a genome browser, specially for smaller genomes, I suggest your try Artemis (http://www.sanger.ac.uk/science/tools/artemis).

You can start right from a FASTA file for your genome and a BAM file for your mapping. It has good visualisation and it's light weight. 

Anna 

Hi Jordan,

you should definitely try Roary (https://sanger-pathogens.github.io/Roary/) and you can also play with PANGP (https://academic.oup.com/bioinformatics/article/30/9/1297/237096/PanGP-A-tool-for-quickly-analyzing-bacterial-pan) if you like.

Hope that's helpful

Dear Mollie,

RAD-seq novice here ;) I'll try to help :

As far as I know it is always good to get replicates, no matter if you cannot necessarily duplicate everything. I would suggest you to select those as you think the most "sensitive" individuals (according to your knowledge about your sampling design, quality of DNA extractions, theoretical expectations, etc) and also a few "good quality" individuals (e.g. with an amount of DNA that is still above your platform technical recommandations).

Comparing "good" duplicates might allow you to estimate (even grossly) the amount of allele/locus dropout you might in your outcomes. Others duplicates could just serve as supplementary information to corroborate your main data.

If you did not read this yet, I recommand you this great paper from Alicia Mastrettra Yanes et al. They made a very good use of RAD-seq duplicates.

Cheers !

Chrys

Finding a closest relative is one thing. If you want to use it as a reference for your own annotations, you should also take into account the quality of the annotations.

Many less-well studied organisms only have indirect annotations based on model species. If those are your closest relatives, you could as well go back to a slightly more distantly related species with more trustworthy annotations.

You may get an idea from this review:

Also check 1kp project. Currently there are many papers coming out using the data from this project. Some of the results can't be obtained using RADseq. 

I think one advantage of RNA-seq data is that it can let the researchers to investigate the evolution of genes in a family.  Here is one example:

Using RADseq data, you couldn't get these information. 

First you need to annotate your sequences to find out from which genes/functions each sequence belong. Then you can rarefy the gene count table directly, it is much more efficient, fast and as roubust to do it like this. Then you need to run proper statistical analysis to mine your data properly. Good luck :-)

Hi,

Thanks for your answer, it gave me a good idea.

What I did was to download all K. pneumoniae genomes and then use a program called Kleborate (https://github.com/katholt/Kleborate) and automatically type all the genomes. Now I have a database with all the uploaded genomes and their MLST.

As the information you provided you have the fasta sequence file then you can just translate it by any online tool like EXPASY, you will get the translated sequence and then you can get the any longest amino acid sequence with out any stop codon then count the methionine amino acid number and then you can get the location of ATG(start codon) and stop codon.

hope you get this..

Thank you for answer. I actually know the programs you have presented, however they do not meet my expectations completely. I have just wrote a script in Python which does what I want.

I intend to develop it and it is possible that I will pipe it with SAM or BAM tools if needed.

It works fast enough. It processes 1MB nucleotide sequence per 1 s on my old dual core 2.1 GHz laptop.

You can make your 100,000 transcript sequences of your sequences in fasta format as your local database and do nblast search in the local database using linux command and loading blast module.. 

thank you. I'll try them.

The easiest way would be to parse GFF/GTF file of your genome to find your snps.

Do you think merging the scaffolds (BACs and genomic scaffolds) would be helpful? There are various ways of doing that.

The short answer is "no".  The proper selection of k-mer size influences the correct assembly of contigs.  Which afterwards, can be verified.

Here is a recent paper that includes references to programs designed to optimize the correct k-mer size for assembly and proposes an alternative empirical method to determine the proper k-mer size to use..

Optimizing k-mer size using a variant grid search to enhance de novo genome assembly. Soyeon Cha and David McK Bird. Bioinformation. 2016; 12(2): 36–40.

Published online 2016 Apr 10. doi: 10.6026/97320630012036

Hi Qinhong

Some points:

- At the first step, it is necessary to recheck the read quality and average coverage, to ensure the use of high-quality reads.

- Also, it is difficult to choose the best assembler for bacterial genomes, so you can try some others instead of SPAdes such as REAPR or FRCBam, But also I prefer the SPAdes. This link can help you: www.cbcb.umd.edu/software/imetamos.

- To remove the adaptors, I suggest to re-trimming.

- Moreover, you can have a look at enclosed papers.

I hope it will be useful

Dear

I could add also these

i think, through visual inspection you can write out the signature sequence of the motif.

the signature sequence can be reported without the need to provide supplementary information of the search result.

There is no "perfect" pipeline for variant calling. Most, if not all, variant callers have systemic errors due to unpredictable behavior of BWA aligners when repeated sequences are encountered. For this reason, it is advisable to mask repeats PRIOR to alignment. K-mer based approaches do not handle variant dense regions very well.

Did you check this question linked below?

You also have the option with the Biostrings package (link below too)

Also, try to install EMBOSS locally and check on the REBASE options

Use Infernal with the latest Rfam library. It is generic for any ncRNA

Hey there,

This might help. You can use CoGe to create synteny maps to aid in identifying collinearity between genomes. You can create an account and upload your genomes privately. Since you are working with bacteria the this should be pretty painless.

Adam

Thanks Volkan for the informative explanation!

Actually I am going to convert coordinates from RefSeqGene NG to Hg19 genome assembly and vice versa through developing an App, but, there is a problem with variable gene lengths in hg19 and NG. Do you know any tool able to handle it?!

You can build phylogenetic trees in whichever way you prefer. For what I remember branch lengths are not used but recalculated by codeml. It is mandatory to align the proteins and then perform the alignment of the coding sequences following that as with revtrans (http://www.cbs.dtu.dk/services/RevTrans/), or alternatives such as MACSE http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0022594. Only in this way you are sure that the aligning algorithm does not introduce frameshifts that would create artifacts in the ensuing selection analysis. Also, as codeml basically will count and model mutations, it is important that there are not too many differences in the sequences; if you not, many of the sites might have had multiple mutations that you can't observe, and the selection level calculation will be affected. You can somehow control for this taking into account the branch lengths of the input tree, usually in substitutions per site, you should have much less than one for every sequence in the multialignment. Clearly the tree has to be built on nucleotide sequences. If there are highly variable regions, you can remove problematic regions from the alignment, but you need to be very careful in doing this because you have to reason in coding sequence terms such that your edit unit will be of three nucleotides, following the frame.

It is also important to contrast the likelihood of the model with selection to the one of the model with no selection. If they do not differ much then, even if you observed a few sites under positive selection, you can't reject the hypothesis of no selection.

Go for I mutant (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) you will get the free energy change value and whether the mutation/SNP is stable or not. In order to visualize the structure why don't you submit the SNP containing sequence in online modelling softwares (http://raptorx.uchicago.edu/ ) or try Swiss model.

you may want to use ftp://ftp.ncbi.nlm.nih.gov/ 

file with the extension ".faa" are protein coding region, have fun!

It happens because the map coverage between these two genes are continuous (it can happen with 2 or more genes) and cuffdiff can't differentiate them. If you have high transcriptome coverage and genes very close to each other, you'll see this lot! Other reason can be the wrong gene prediction during structural annotation. Some gene predictors split real genes in two, or more sometimes. Especially if you used a small number of predictors.

To avoid this intergenic mapping, use the option -T on tophat command line.

Tophat manual:

"The following options in this section are only used when the transcriptome search was activated with -G/--GTF and/or --transcriptome-index.

-T/--transcriptome-only Only align the reads to the transcriptome and report only those mappings as genomic mappings."

From post processing done on the data, it looks that way.  But some of the regions are 40 kb, which doesn't make much sense to me.

Thank you so much Mr. Pucker, i will try it.

Hi Anna,

Also, you can find the homology of kinase substrate consensus sequence using SwissModel database for predicting real homolog target.

Orthodb (http://www.orthodb.org/v9.1/?query=Mitochondrial%20sequence) past your consensus to find similar.

Another mitochondrial db. (http://www.mitomap.org/foswiki/bin/view/MITOMASTER/WebHome)

Hope it is useful.

Regards,

Rajesh

Thank You Lexa Sir. To be more specific,I want to ask you that for COG analysis on windows platform ,it is required to install COG software available on ftp site or it is a database from where I need to mine out my genes ?

Hi,

1) If you are interested in the quality of the reads, you could apply fastQC.

2) There are many different tools (https://omictools.com/assembly-evaluation-category) to evaluate the quality of an assembly e.g. QUAST (http://bioinf.spbau.ru/quast). QUAST will provide some basic statistics about your assembly and run a complete evaluation of the assembly. If you are just interested in the very basic statistics of your contigs, I could send you a simple python script to calculate them.

You can use R/Bio-conductor for other format conversions and also for micro-array data analysis. Check out the following links.

I know that.. the problem was made before then, when someone selected a software package that was not properly evaluated for meeting your needs and resources (otherwise you wouldnt be asking this question). Anyway.. for alternatives like those I was pointing out I would be more than happy to help.

Best of luck

You can use cufflink for denovo that can infer transcripts de novo from the mapping data alone. There are some other algorithms that quantify expression from transcriptome mappings include RSEM (RNA-Seq by Expectation Maximization) , eXpress , Sailfish and kallisto etc. TransDecoder can be another option http://transdecoder.github.io/

go for MISA software 

Thank you both for your responses.

@Michaela I will try other aligners.

@Moinul Both genomes are assembled in scaffolds. AG1-IA is working fine. I'll try your suggestion if I don't succeed with other aligners.

E-value tells you the expected number of hits with a given score or higher.

At low E-values (between 0 - 1e-3), you typically use the numbers only to rank hits, e.g. to select the best one among many. Because otherwise, they are all significant (in the sense that they are not random noise). Hits with E-value > 1 are usually ignored, unless you know more specifically what you're looking for and that it may have very low similarity. In that case you must filter the hits that are satisfactory according to some other method (other than sequence similarity) or just take your chances and accept many false positives. Hits with intermediate values (between 1e-3 and 1) are borderline significant, how to treat those may be different from case to case.

I personally prefer using EdgeR to perform DE gene analysis. With regards to performing GO enrichment analysis on the DE gene list you obtained from the first step, you can either give goseq (http://bioconductor.org/packages/release/bioc/html/goseq.html) a try, or use publicly available GO enrichment analysis tools such as PantherDB (http://www.pantherdb.org). 

However, I am not able to understand your question properly, but main thing which i would like to bring to your notice is, you cannot submit the data in NCBI if already exist as a part of another project. Though you are doing modification in existing data but the fact is, it already exist in NCBI database and people who are actually interested in studying wilt transcriptome and chickpea transcriptome can bifurcate by mapping the reads to wilt genome and chickpea genome respectively.

But in case if you have your original transcriptome data generated for wilt infected chickpea by your own then you can map total reads to existing wilt genome and can use mapped reads for recreating wild genome by denovo assembly and now you can submit it to NCBI as wilt data.

Hope it helps

Dear all,

We are doing RADseq on Trichogramma unique individuals, these "beasts" are ca 0.3 mm long !!!. To do so we use Qiagen individual extraction followed by WGA and it works very well. We tested the reliability of WGA and we did not detect any problems. To do so we let a male (M1) and a female (F1) tricho mating. We obtained multiple female descents (F2s) that we isolated (no mating) and let them produced males (M3s). We pooled all these males (n=ca 2000, in a kind of natural genome amplification) and compare the results with WGA of  M1 and F1. However, for some reasons we had poor results using WGA for UCE capture, in average we lost one third of the loci captured on WGA individuals.

Best

JY

Me and my colleagues (Karel Sedlar, Helena Skutkova) from Department of Biomedical Engineering at Brno University of Technology are working in the field of genomic signal processing. You can check our contributions if you're interested. 

As expected, this is microarray data. Usually one probe is used to detect one gene (transcript) in an array but it's also common that some genes have multiple probes designed to hybridize to different regions of the transcript, perhaps for the purpose of variant/isoform studies. If you are interested in the overall expression of the gene, I would usually take an average of all probes for that gene. Otherwise you could look up the information of the probes (their locations, etc.) from the array company. In your case, http://www.affymetrix.com/estore/catalog/131537/AFFY/Human+Genome+U133A+2.0+Array#1_1.

In which format did you export them ?

You'll need the Sorted Bam Format

"Standard library preparation methods include a PCR enrichment step prior to cluster generation. Biases inherent in PCR amplification result in uneven read coverage and increase the numbers of duplicate fragments present in the library."

Colleagues have recommended AliView, I haven`t had the time to road test it but it might be worth a look.

You can find information about it, and links to download it, here:

Finally found the answer.

From Heled and Drummond 2009 (the paper where starBEAST is described)

"Multiple samples per species are necessary for a complete estimation. Even two samples per species are sufficient, given enough loci. A single sample means no coalescent events for that extant species and so no information to estimate population size. This may in turn have a detrimental effect on inferring speciation times and perhaps even species topology."

I would definitely recommend BS-Seeker2 (http://pellegrini.mcdb.ucla.edu/BS_Seeker2/)

OK, let me suggest a rather more optimistic assessment. :)

(1) 16S rRNA gene sequencing is fast and ridiculously inexpensive. I would say it has limitations rather than "failures," since its success rate is nearly 100%. The limitation is what you say: the sequence is so highly conserved that it sometimes lacks enough phylogenetic information to pin the isolate down to the species level. Genus, certainly, but not always species. This isn't a matter of accuracy, but precision. For many applications, it is precise enough. Single Nucleotide Polymorphisms (SNPs) certainly exist, but they are a "feature," not a "bug." One SNP out of 1400+ bp of data won't skew the results. But a set of common SNPs does indeed indicate a close phylogenetic relationship.

(2) Single gene comparisons are risky for the reason you mentioned--horizontal gene transfer between bacterial species. However, in practice what we observe is that highly conserved housekeeping genes are less frequently exchanged by HGT than are mobile elements and other components of the pangenome. Careful comparisons with large datasets reveal that there is a high level of correlation between housekeeping gene sequences, 16S rRNA gene sequences, and whole genome sequences (WGS). Single gene sequences can often, then, be useful surrogates for WGS in determining the taxonomic identity of an isolate. And because they are less conserved than 16S rRNA gene sequences, they can usually identify genes down to the species level.

(3) MLST is indeed a highly sensitive, highly accurate method to classify bacteria. Again, only housekeeping genes are chosen. It isn't "luck of the draw," really. Several thousand bp of data are a good sample size, and the incorporation of multiple genes safeguards against the risks of single gene comparisons noted above. I maintain a Bacillus subtilis group MLST database on the MLST website created by Keith Jolley at Oxford. I've done extensive (unpublished) statistical analyses comparing phylogenetic distances computed by MLST and WGS comparisons, and the correlation is extremely high (R2=0.986 for my data). That gives me a high degree of confidence that MLST gives very meaningful answers for bacterial classification.

(4) The reason that MLST is being superseded by whole genome comparisons isn't that they fail; it is that WGS technology is developing so rapidly, and the costs are dropping at the same rate. Why not get the whole genome sequence if you can afford it? I completely disagree with your characterization of WGS comparison as providing meaningless output. Instead of throwing up your hands in despair, I encourage you to read the papers and learn what the numbers mean! They actually convey an amazing amount of information. And current best practice algorithms, like the Genome to Genome Distance Calculator of the DSMZ, aren't at all perturbed by the type of situation that you describe, where one subset of genomes has a mobile element that another lacks. They don't even require that all genomes are complete. The resulting phylogenies are highly accurate and exquisitely precise.

So there you have it. :) In my view, we are in a golden age of bacterial species identification, thanks to the revolutionary developments in sequence-based technologies and bioinformatics applications. If you only have a few dollars at your disposal, do a 16S sequence. It will give you the proper genus and will narrow down the species possibilities to a small group. If you have a couple of hundred dollars, you can do a very thorough MLST and know exactly what species you have, and even determine which isolates within the species are the closest relatives. And if you have more like $1000-$1500, go ahead and do the WGS. Not only will you be able to identify the species with total confidence, you will acquire deep insights into its physiology and ecological role as you examine the sequence. And these price estimates will only come down over the next few years.

So cheer up! And happy sequencing. :)

Hi,

Try any of the following:

1. ACT - http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act

2. BRIG - http://brig.sourceforge.net/

3. circos - http://circos.ca/

Best regards,

---

Diran

Hi Jia Zhou , I am doing something similar so if you ever came up with an answer could you let me know the methodology that you followed. Thanks 

Before cutting the sequences, blast against the nucleotide database to see the reference sequences and compare it have a optimum size range with good chromatogram quality.