Next Generation Sequencing - Science method

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Also, which is better (or most widely used) for metagenomics NGS, FLX or Illumina?

In my opinion, when the analysis is made by the NGS, the similarity of the 16S rDNA in environmental bacterias gives place to a bigger diversity than the real one.

I am wondering if people are still resequencing regions flanking an associated SNP, in order to identify hitherto undiscovered common variants which may be the causative SNP? Or is this a bit of an out-of-date strategy after the release of 1000G data?

Using small RNA libraries and Illumina sequencing, I would like to detect the differentially expressed small RNA in plant samples and understand their biological meaning and function. I'm using CLC Genomics Workbench 5.5.

Could a scheme of different analysis steps be done, which could be helpful for beginner in the Next Generation Sequencing data analysis?

Given two or more reads library sampled from their respective tissues (e.g. cancer or non-cancer), we have a method that can assign/classify the reads to their respective 'source genome', but without mapping to the reference genome.

This is inspired from the fact that there are no-identical genomes. Mapping them will lead to loss of information.

This method also considers error rates in the reads and coverage.

One possible application is to use it for finding rearrangement breakpoint reads.

What are the other potential application of such methods?

I have used Nextera library kit for the first time and had issues with the reads coming out of HiSeq 100 being GC biased .... but only for the first few base pair reads. The majority of the reads is good. Just wondering if it is a common issue with Nextera kits.

I am looking for some DNA to use as an internal standard when we do sequencing. I need to find a species that has never been found in the human gut, but has a medium GC content. If you know where I can order the DNA from, that is even better. I have been looking for something like Nitrosomonas or Rhodopsuedemonas, but I am having trouble finding a good source. Any suggestions?

A new wave of scientists from the systems biology field suggest that drug discovery can be improved by the use of biomolecular networks. Can they make it? Should they try? Is it the future of drug discovery?

See what one of them wrote in the NYAS website:

"The most pressing unmet medical needs correspond to complex diseases caused by a combination of genetic and environmental factors. Traditional drug discovery strategies ignore the complexity of biological systems, screening compounds on individual targets rather than focusing on biomolecular networks. Despite growing evidence that the conditions we aim to treat are complex and require the development of treatments that exhibit polypharmacological properties, current drug discovery programs still rely on simplistic approaches during compound selection. Complexity is then considered during the development phase, where the costs and risks are much higher than in the discovery phase. This symposium aims to challenge the "one-target, one-disease" tradition and to discuss design and implementation of biological assays featuring multiple target strategies during the primary discovery steps." (source http://www.nyas.org/Events/Detail.aspx?cid=073a364a-af58-49e0-896e-499e51427b66)

Let's say I ran forty cancer samples, twenty responders and twenty non-responders to some treatment. I performed alignment and annotation of SNVs and indels in each sample. Now I want to know if there are any deferentially affected genes in the two groups that may justify the different response I observed in those tumors. Problem is, simple chi squares won't do, because a single gene can be affected by multiple potentially deleterious SNVs and indels, and I may have to bin multiple alterations to have enough power. I may therefore tag each gene as affected/unaffected by a potentially deleterious variation, and proceed this way with my analyses. But how do I know if a "potentially" deleterious variation is really so? It's impossible to validate biologically all of them. Or I may restrict my analysis to variations that recur more than once in two different samples, but then again, some genes can be altered in many places, and in one place just once out of several samples, and I would overlook them.

Finally, I may consider not genes but pathways, and see whether some pathways are involved by deleterious alterations in responder tumors and not in non-responders, or vice-verse, but pathways are not as well defined as one may want to think; there is a risk of including deleterious variations that have nothing to do with the underlying biology of my samples. It's more philosophy than statistics here. What would you do? What do you you recommend?

Special micro array data related to plants infected by pathogens.

I have sequenced a bacterial 16S rRNA coding region using capillary sequencing and then got the De-Novo assembly (DNA sequence) of the same bacteria (same pure culture). After a blast search I wasn't able to find any hit between these two sequences. Can anyone suggest the reason behind such a disconnect?

Next Generation Sequencing (NGS) technologies dropped down sequencing costs and times for obtaining data. Now it is almost easy to obtain the complete genome of an organism of interest (faster if re-sequencing). Comparative genomics could then highlight features making our strain of special interest. How affordable is it to apply this vision at large scale through public health microbiology units? Do public health systems working in such a way exist?

Does anybody know how to check if a new SNP is on ANY chip? I am looking for experiments in which a specfific SNP has been evaluated.

With the current Next Generation Sequencing, how far does the necessity of chromosome walking go?

What is the benefit of using NGS vs microarray technique for DNA methylation? Seems current methylation arrays, e.g. infinium humanmethylation450 used by TCGA, already provided very high density of coverage, although not down to each single nucleotide, but good enough to discovery methylation pattern along the genome. So is there any reason we have to use NGS rather than microarray for DNA methylation studies?

M220 is great for shearing DNA, but I have heard that it may not be as good with chromatin, and that it is a safer bet to use S220 for that. I would like to have an instrument that has both capabilities, but S220 is so expensive. Any thoughts/suggestions?

We have very small samples from snap-frozen biopsies and need to perform several analyses with them. I have been against preamplification, since I'm afraid it would introduce more bias in an already tricky specimen.

What are your personal opinions on the subject of total RNA preamplification for microarray and other transcriptomics (e.g. transcriptome sequencing) applications?

I have lots of sequencing data for a particular cancer I am studying, and I am bit confused about detection of gene fusions. Please can anyone suggest what are the methods and tools available and which is the best in terms of identifying gene fusions in incompletely annotated genome?

I'm looking for tools working under windows environment with java that could be used as atlernatives to vcftools or even GATK.

Difference between old and new version of samtools

I can't find anything on the Affymetrix website.

Num. reads assembled: 1669184

Avg. total coverage: 16.60

Number of contigs: 13991

Largest contig: 112360

N50 contig size: 1171

that is the summary I got out of a de novo assembly (ion torrent data and MIRA3 assembler

I would like a suggession as to how you would rate the sequence.

Bacterial genome, expected to have a few plasmids.

How will you rate the quality of this sequence?

I have tried it with Newbler, which are like

numAlignedReads = 1647777,

N50ContigSize = 955,

numberOfContigs = 26930,

largestContigSize = 129390;

what do I do with these now... suggestions required.

I have been working on data analyzed by Samtools or GATK, and in both cases indels were badly detected. More especially, duplications or small insertions were missed by the two tools. I had already heard about this problem, but I had thought that I would rather see false positives than false negatives. Do you know any algorithm that performs better regarding this precise point?

To my knowledge, 11 types of cancers have been surveyed by whole-exome sequencing: leukemia, ovarian cancer, head and neck squamous cell, renal carcinoma, melanoma, pancreatic cancer, gastric cancer, colon cancer, prostate cancer, lung cancer, brain cancer, and breast cancer. I do not count whole genome sequencing or RNA-Seq. Do you know any tumor types I missed?

Is there any algorithmic approach to find the adapter traces within the reads efficiently.

I want to see differential expression, novel transcript, novel splice variants, and some information about NON coding RNA s too. Should I enrich my sample with poly Ts then, or just go for sequencing without any mRNA enrichment. Let me know the pros and cons of both of the strategies.

We do not have access to liquid nitrogen or dry ice, so I need to determine another method of storing fresh tumor biopsy material for up to 48 hours prior to DNA extraction for whole exome/genome sequence. Can the samples be stored at room temperature? Does anyone have any suggestions?

I have a GFF file containing positions of annotations (Transposons, mRNA, etc.), and the FASTA file of the reference genome (i.e. Vitis vinifera).

I would like to transfer the GFF into a FASTA file containing the sequences corresponding to the information included in the GFF.

I intend to perform Illumina sequencing of my RNA samples, but due to degradation problems I would like to add RNase Out to the sample to improve its quality. Is it a problem for Illumina platforms?

There are many ChIP-seq peak callers available, such as MACS, ERANGE, SISSRS, CisGenome et al. Which one do you favour and why?

We got a typical pedigree samples (n=10, and 4 patients). How to make a reasonable genetic analysis to search the candidate genes? With STR marker? SNP array? SNP+ CNV array? Exon sequencing? Given limited funding.

Is their any identifier within the sam files which can give me the number of reads aligned to a particular chromosomal region.

I would like to use a DB that is exclusively human and a perhaps separate one that has been experimentally established to aim at bacteria/viruses. I would like to sieve through some NGS data and compare RNA interference profiles between healthy humans and disease carriers.

Due to the specific BLAST algorithm, short sequences cannot be used as queries. Thus, I wonder if there are other methods to search homology of short sequences with ESTs or mRNAs. For example, can BLAST performed after the sequences are assembled in contig?

I have a dataset of reads collected from Illumina platform.From these raw data I want to generate high quality read dataset. Can anyone suggest me any freely available softwares.

Does anyone have feedback on iron torrent technology for detection of CNV (copy number variation) in small genome (like yeast)? Is this technique really cheaper than illumina?

While processing Illumnia paired-end reads, dataset of mine gave rise to both - paired-end and single-end due to read filtering based on phred score. Is there any way to map both dataset into the reference genome to generate single sam file using bwa aligner?

Generally in illumina paired end sequences we are missing out on het indels and looks like its a common problem. Can anyone suggest something to overcome these and which could be helpful in calling these het indels.

I mean due to their algorithm some aligners tend to align methylated sequences to the genome more efficiently or vice versa. Can anyone explain with some examples please.

Xna

Loven et al. 2012 (Revisiting global gene expression analysis. Cell 151: 476-482) addresses a significant issue with current expression profiling methodologies - namely, the untested (and apparently unwarranted) assumption that cells being compared produce equivalent amounts of RNA/cell. We have previously addressed this issue in a different context (plant polyploids - Coate and Doyle 2010, see attached reprint), and demonstrated that an allopolypploid has 1.4x more RNA per cell than its diploid progenitors. The fact that various other cell types (including tumor cells, embryonic stem cells, lymphocytes) are also likely to vary dramatically in RNA/cell (transcriptome size) suggests that transcriptome size variation is a significant issue affecting many expression profiling experiments.

Loven et al. propose an RNA spike-in approach to address this problem, which they demonstrate to be effective with cell cultures. In Coate & Doyle (2010) we described a different approach that also works with solid tissues and doesn't require cell counting. Any feedback on our approach would be welcomed!

Finally, a question: with the typical normalization methods used today (e.g. RPKM for RNA-Seq), expression is reported as a fraction of the total RNA input. As explained in Loven et al. (2012) and Coate & Doyle (2010), this estimate (which we call expression per transcriptome) can differ significantly from expression per cell. My question is which of these measures is biologically most relevant? Absolute expression per cell (or per gene) might seem the obvious choice, but given that gene products interact with other gene products in a cellular context, expression relative to the expression of other genes (as estimated by RPKM and standard microarray normalization methods) could be what determines the biological response. Any thoughts?

dears: I'm working on chip-seq data. I've downloaded sra format data. to analyze I have to convert them to fastq format and then to SAM file. I made a fastq file but I cant convert it to SAM file? who knows how I can do this?

Developing a a good CLIP-Seq methodology

I could imagine that a SNP on X in males would be called ref/ref or alt/alt, but not ref/alt...

I have to isolate total RNA from needle biopsy tissue samples for the purpose of microRNA profiling by using next generation sequencing. I have purchased Ambion mirvana kits but after repeated attempts (I have used 6 different tissue samples so far), I am unable to obtain a good yield and OD values. The maximum yield I am getting from 4mg tissue is no more than 70-120ng/ul when dissolved in 50ul of nuclease free water and 260/230 value is always from 0.75 to 1.8. I have tried different steps to optimize yield from mirvana kit but with no success. So anyone who has experience with this kit please share your protocol so I could possibly improve total RNA from my samples without wasting anymore samples. These samples have been stored in RNAlater and there is no degradation as measured by Bioanalyzer (RIN>8).

SNP array? SNP array + low coverage genome sequencing? CNVs?

We want to know if it is an available method.

I failed to make it on either Illumina PE, Hiseq2000 (stuck at mery) and Roche 454 FLX (stuck to create overlap)

I'm trying to compare gene expression profiles using RNA-seq (100 base, Single Read). I want to multiplex my samples but I'm not able to determine the coverage.

I'm working on human cell lines.

In my lab, a target gene was knockdowned (RNAi). Both RNAi and Control samples were separately prepared to construct libraries of small RNAs that were deep sequenced using Illumina platform (36 cycles). The sequencing results (performed in triplicate) showed a clear differential pattern in the length distribution of reads when Control and RNAi samples were compared. Most reads from RNAi samples ranges from 10 to 17 nt and many of them are rRNA. On the other hand, while reads generated from Control samples are longer than 20 nt. What these differential distribution patterns mean? How to explain the high abundance of ribosomal RNA in the RNAi samples and low abundance in Controls? Have you similar results and/or advices on how to interpret these data? Do you know any article discussing it? Please, share your expertise and beliefs.

I have an hybrid data of 454 + Illumina + SOLiD which i have assembled into 157439 contigs covering around 1.3 GB genome. Now I want to create scaffolds using 20kb (3 libraries) 454 paired end reads and BAC sequences.

Which tool can I use? I want to assemble genome of 850 MB.

List of the algorithms in a compact form.

What refined analysis is possible and are they reliable?

I heard that there is a place in China that performs about a quarter of all Illumina sequences in the world, do you know the place? Also of course if there are other worthy places I would be happy to hear about them....

Thanks a lot...

I am trying to assemble hybrid reads using PE and BAC reads. I have shredded the sequences 1Kb with 800bp overlap and I am assembling with Newbler. I am giving input of 1 GB but getting output of 500 MB. Can anyone suggest an alternative method?

I'm looking for a software tool to call copy number variations (CNV's) from NGS exome sequence data. There seem to be many possibilities (CNVnator, ExomeCNV, cn.MOPS, control-FREEC, BreakDancer.... etc.) Has anyone compared or evaluated any of these tools? Any recommendations for something that's easy to use, speedy, and accurate?

I would like to know what the general consensus is regarding cut-off values for gene expression fold changes (is it mainly >2 up and down-regulated?). Also, is this cut-off applied together with the cut-off for p-value which is p<0.05?

I'm looking for alternative software for gene and functional prediction, metabolic pathway construction and etc., is anyone willing to share their experience in yeast gene and functional prediction?

I have analysed my NGS data from Illumina (RRBS), I have fragments of information (chromosome, genomic position etc) and I would like to search against databases and see whether there are any common SNPs present in my fragments. I presume I would find DbSNPs (131 or 135 may be), but if anyone details the process or can give me advice which will enable me to do this search quickly that will be much appreciated.

From where I can get the complete information regarding next generation sequence/deep sequencing data analysis ?

Is there anyone interested in transcriptomic analysis of Sporothrix schenckii?

Unfortunately my research group is not able to use NGS and I would like to sequence the cDNAs of this very important pathogenic fungus. Currently there are no data about transcriptomics of S. schenckii.

Anyone interested please contact me.

Orazio

I am planning to do Illumina HiSeq sequncing on a relatively small number of exons for 96 individuals (snakes). I am thinking about using the Illumina's Nextera 96-indexing DNA library prep kit for barcoding and prepping the sequencing libraries, in combination with the MySelect kit for custom sequence capture. These 96 barcoded sequence capture libraries will then be pooled for a single lane on the HiSeq2000. Has anyone tried the Nextera kit and the MySelect kit in combination? If so, was it successful? I am most worried about the small starting amount of DNA for the Nextera kit causing targeted exons be missed. Thanks for any advise you can provide.

I cannot make newbler run GUI after installation and restart, can anyone can help me solve such an issue?

I'm using CentOS 6.2 and Redhat 6. Both have the same problem >_<

I'm still newbie in NGS and my knowledge limited to few software. I'm trying to explorer to more software and method to gain my knowledge.

After quality filter, assembly, and validation. Is there is anything else or tiny thing that user could missing up. I'm wandering, the exact definition for advance assembly analysis

Looking for user experience feedback

I realize it's a big topic, but I'd be curious to hear people's opinions about the best ways to rank genetic variants obtained from an NGS procedure by their probability of being disease causing. I'm aware of SIFT, PolyPhen, MutationTaster, etc., but these all seem to give conflicting results much of the time. What tools do you use? What techniques or algorithms have you developed? Is it even possible?

We have developed our own tool for compression, which saves ~70% while creating "regular" BAM files. But we are seeing all these new formats that are space economic - but I want to hear from people who are actually using them if they have hidden caveats and what performance I should be expecting.

Do you know of any software that allows prediction and analyses of mis-assemble and QC of assembly result other than AMOS and CLC software?

During the conversion, the msg indicate the conversion fail, it's lock and lib not found. This situation also happend when I convert yeast velvet amos compatible to bank format.

Did anyone know how to convert Chip-seq CEL files to Wig format?

Does anyone have any recommendations on good papers on this topic? Like "must reads". Can put for any sequencing methods (ion-semi., nanopore, etc.)

Many thanks in advance!

Jon

I've seen numerous articles showing good correlation between their qPCR and transcriptome seq data, however, I have not had such great correlation. Is this more common than most articles report, has anyone else had this problem?

I am trying to align NGS data to repetitive regions. Straight-forward use of tools such as BWA fails. Any suggestions?

Last time I checked the Roche 480 LightCycler made this possible, but it was near impossible to pipette the 1536 plates.... The question is, is there any way to address individual positions (a one-er pipette tip on a gantry robot, for instance) that can actually save money in the lab AND provide high individual PCR throughput? Yes, I know about Fluidigm. Not exactly the same thing. Here is a recent review and I don't see the kind of robotics I am thinking of: http://www.labtimes.org/labtimes/issues/lt2010/lt01/lt_2010_01_54_57.pdf

Is there anything new on this front?

I'm looking for a cost-effective means for validation of potential associations in a GWAS as an alternative to individual genotyping, at least in a first time.

I thought in a paired-end sistem that gives me reads of 250 bp making contigs of 500 bp.

For cancer still I may understand the need ( to capture the variation due to multiple clone), but why it is needed for other disease.

I had 2 runs from the same library at some NGS platform (intentionally not mentioning the name of the platform) and I have ended up with some strange findings. In my second run I found some extra variable regions which were not there in the first.

FYI: I have used the same parameters in the analysis pipeline for both sets of data. Quality scores is above Q30 for all those alternative alleles in the second run. Locus been not reported as variant site before.

What could be the reason behind such a result?

We are dealing with Capsicum and also soon with sunflower transcriptomes. I am not updated in the novelities of assemblers. We will try Trinity and Newbler (if I can make it run in my Mac!). Does anyone have suggestions?

Prediction softwares especially with respect to structure and function

Microarray is still cheaper but covering less genes.

NGS is a little more expensive but covers whole genes.

Hi, are there any suggestions or references to run RNAseq for measuring expression level of only selected genes out of very small amount of material?

I know there are efforts to sequence even single cells, but it seems they are more focused on identifying mutations rather than quantifying expression levels and the technique itself is still under development. Any suggestions? Or maybe we should go back to run qPCR? Thanks.

(In soil samples) which reactives?

I would like to know if the software miRDeep2, for miRNAs prediction from Next-Generation Sequencing data, when checking for a correct Drosha/Dicer cut, takes into consideration the presence of reads mapping on pre-miRNAs containing putative "offset miRNAs (moRNAs)" (to see what moRNAs are please, see the paper by Bortoluzzi et al. in Blood. 2012 Mar 29;119) and if the hairpin sequence is therefore retained or discarded for further analysis.

I fear that the software does not have a check for these particular miRNAs forms and can arbitrarily exclude "real" precursors encoding both moRNAs and miRNAs.

Have you ever encountered this problem?

Troubleshooting ChIP-Seq

What's the general consensus as to whether amplicon pyrosequencing fusion primers should be PAGE purified? It seems often regular that desalting is thought to be sufficient. However, the primers are as long as 58-mers and so there is a concern of errors during primer synthesis. If the oligos miss a portion at the 5' end, they would not have the pyrotag but would still amplify target in 16S PCR. However, in emulsion PCR those targets wouldn't amplify.

As far as I know at the moment, only Sanger sequencing is reimbursed by health insurances of many countries. If the commissions in your region on reimbursement already worked on specific reimbursement for next-generation sequencing, what do they have as a definite concept on what kinds of rules and regulations apply to next-generation sequencing for the diagnostic of diseases?

Today, functional enrichment tools are used to interpret biological data generated by deep sequencing and other high-throughput technologies. Examples are Gene Ontology or pathway analysis software such as Ingenuity or MetaCore.

How may these tools evolve and what new tools should appear in the future, as the sequencing technologies develop and become more popular, even outside of basic research?

There are lots of efforts to use NGS rather than microarray for pooled shRNA screening, my questions: Is there any unique value that NGS can provide us in this type of study that is not available by using microarray? Does it provide maybe better data quality but with the price of longer turn-around time and much more complicated data analysis work?

Let us imagine that some laboratory developed a method for creating restriction endonucleases that can specifically bind to short DNA sequence and specifically cut it or other determined DNA motif. In what applications would such enzymes be required? Could they be applied to Next Generation Sequencing?

If you use restriction endonucleases, describe their usage in few words or just say “I use them”.

I am aware of Zinc-Finger Nucleases or TALENs, but these enzymes only specifically bind target DNA and have non-specific DNA cutting activity.

We're planning to next-gen sequence flow-sorted microbes out of sputum samples. Flow sorting will be done using a fluorescent antibody. Our concern is that the staining involves a formalin-fixation step, and that this might crosslink the microbial DNA, making it useless for DNA sequencing. Does anyone have experience sequencing antibody-fixed/sorted microbial DNA? We'd like to know if there are any pitfalls to this approach before we invest the money. Thanks in advance.

I am trying to compare array snp calls to ngs snp calls, by using the position of the genotype. However the builds used differ, so I would like to make a liftover of the array data to meet the ngs data. USCS offers a liftover from e.g. build 18 to 19, however I havent been able to find a site where I can do a liftover from e.g. build 19.p1 to 19.p9. Is there someone out there that can help me ? Thanks.

My basic understanding of 'paired ended' sequencing is that it adds nucleotides to both ends of the fragment that's to be sequenced. My questions are: for MeDIP-seq, usually how many nucleotides to add on each end? and how are these nucleotides added, since there is no template? are the two 'paired ends' added to the same strand or to two different strands?

Thank you,

Jackie

I am planning to study the miRNomes in different diseases. Does anyone have experience with the analysis of miRNomes using NGS, especially when starting with a low amount (2-10 ng) of enriched miRNA? Any kind of information, especially in the generation of sequencing libraries (with or without amplification) would be helpful for me. Thanks for your efforts! Best, CK

I have an hybrid contigs of 454 + Illumina + SOLiD which is around 850 Mb. Now I want to create scaffolds using SSPACE with 20kb (3 libraries) of 454 paired end reads which have been converted into Paired end fastq reads (linker were removed).

Can anyone help me out with this or suggest some better approach for scaffolding?

If I wish to do whole methylome study of human what should be my chipseq platform of choice and why?