Find Sequence In Fastq File

[Ellyn Krucke]

Thishas the advantage that you can specify, for example, hdist=1 to get all the reads that contain the sequence with up to 1 mismatch, it works for formats other than fastq, and it also looks for the reverse-complement (unless you add the flag rcomp=f).

Hello, I have a 48 GB FASTQ file and I would like to extract from it, the sequence coding for ANY protein. If anyone has any recommendations as to how I would do this I would really appreciate it. I tried using galaxyproject to convert it to a FASTA file to start with, but the file is too big. Also, I am using a macOS. Background: It's for a class project comparing a protein (bioinformatically) from an extinct mammoth species to its living relatives. It's just for practice purposes/learning with the focus on the protein itself, but this first step is proving to be the most difficult. Thank you for your help :)

I think most people on this forum will tell you that you should solve your homeworks independently. That is the whole point of learning, and if your instructor(s) wanted to give you hints, they would have done so.

To get you started, SeqKit can convert fastq to fasta format. If that doesn't work for some reason, BBTools is Java-based and has programs that can do the conversion. From there you should be able to figure out which flavor of BLAST will let you search that fasta database.

I will add one more idea. Once you get the DNA sequence of the protein you are looking for you can align (using a NGS data aligner) the reads against that sequence and then simply extract reads that map from the alignment file. While this is generally not recommended when you have sequence from the entire genome, for your exercise that may be enough.

FASTQ files are generally raw reads, and you would not generally look for protein sequences from these raw reads. Possible reasons: short reads would only have fragments of the protein sequences on them, and long reads like pacbio/nanopore often have errors that make them not really suitable to use in their raw form (short reads also have errors). Either way, it's not common to look for protein sequences directly on reads to my knowledge.

1) (harder) use a de-novo genome assembler, to assemble your FASTQ reads into a genome assembly (FASTA), then, you would run gene prediction on your genome assembly. This would give you your protein sequences. doing a genome assembly is quite laborious. de-novo genome assemblers include miniasm, abyss, etc. gene prediction tools include MAKER

2) (easier) align the reads from the FASTQ file to a species that already has a reference genome e.g. (this is similar to what GenoMax suggests). and then you can check e.g. where is the P53 gene on the elephant genome, and how do the reads from the mammoth genome align in that region. you could determine the variants using a program called a variant caller. this process has caveats--you have to be careful interpreting the data, as you are aligning reads to a totally different species. but, generally in all data analysis, you have to be careful and be aware of caveats. aligning reads can be done with tools like bowtie, bwa, or minimap2. variant callers include tools like bcftools or gatk. you can then determine the effect of those variants using a variant effect prediction, this can tell you the consequence on the protein using tools like ensembl VEP or SnpEff.

Hi, Im working with a 4c-seq experiment and one of the issues with this technique is that you have to delete reads that originated adjacent from the sequence of interest, I have the sequence corresponding to these reads, I need to delete these reads, till now I only found the reads that have this sequence using vmatchpattern but how can i delete these reads and create a new fastq archive without them?

Suppose I wanted to get rid of sequences with "TTACC". I could use the grepl() function to find the reads that do not contain this pattern, and subset the original reads to get those that I want to keep

We showed a little how to search within a file using less. We can alsosearch within files without even opening them, using grep. Grep is a command-lineutility for searching plain-text data sets for lines matching a string or regular expression.Let's give it a try!

We get back a lot of lines. What is we want to see the whole fastq record for each of these read.We can use the '-B' argument for grep to return the matched line plus one before (-B 1) and twolines after (-A 2). Since each record is four lines and the last second is the sequence, this shouldgive the whole record.

We're excited we have all these sequences that we care about that wejust got from the FASTQ files. That is a really important motifthat is going to help us answer our important question. But all thosesequences just went whizzing by with grep. How can we capture them?

We can do that with something called "redirection". The idea is thatwe're redirecting the output to the terminal (all the stuff that wentwhizzing by) to something else. In this case, we want to print itto a file, so that we can look at it later.

The prompt should sit there a little bit, and then it should look like nothinghappened. But type ls. You should have a new file called bad_reads.txt. Takea look at it and see if it has what you think it should.

There's one more useful redirection command that we're going to show, and that'scalled the pipe command, and it is . It's probably not a key onyour keyboard you use very much. What does is take the output thatscrolling by on the terminal and then can run it through another command.When it was all whizzing by before, we wished we could just slow it down andlook at it, like we can with less. Well it turns out that we can! We pipethe grep command through less

We can also do something tricky and use the command wc. wc stands forword count. It counts the number of lines or characters. So, we can useit to count the number of lines we're getting back from our grep command.And that will magically tell us how many sequences we're finding. We're

The philosophy behind these command line programs is that none of themreally do anything all that impressive. BUT when you start chainingthem together, you can do some really powerful things reallyefficiently. If you want to be proficient at using the shell, you mustlearn to become proficient with the pipe and redirection operators:, >, >>.

That's only the first line but it is a lot to take in. 'cut' is a program that will extract columns in tab-delimitedfiles. It is a very good command to know. Lets look at just the first four columns in the header using the '' redirectand 'cut'

We can use some new tools 'sort' and 'uniq' to extract more information. For example, cut the third column, remove theheader and sort the values. The '-v' option for greap means return all lines that DO NOT match.

I have a large fastq file and I want to add the sequence "TTAAGG" to the end of each sequence in my file (the 2nd line then every 4th line after), while still maintaining the fastq file format. For example:this is the first line I start with:

I've inherited some fastq demultiplexed paired end files and I'm uncertain if the primers are still in these raw files. Is there a way for me to identify them in the raw files or do I need to ask the sequencing center if the primers were sequenced?

You will need to just check the files to be sure, though asking the sequencing center may be more straightforward (especially if your primers contain degenerate bases). If you have the raw .fastq files (before importing into QIIME2) and your primer does not contain degenerate bases, you could type the following command into your terminal (replacing ACGTACGT with your actual primer sequence):

That will list the number of lines your primer sequence is detected in, which should give a a pretty good idea (if the number is very large, or precisely 1/4 the length of the total file, then your primer(s) are still in the reads). If you do have degenerate bases, you could use BLAST to search for your primers in your sequences (we still don't have a method to do this in QIIME2 on raw sequences, just FeatureData[Sequence] data, but may support this in the near future). The easiest/quickest way to do this would be to just BLAST the first few sequences in the file (unless if you can think of a reason why you'd need to BLAST them all). Pull out the first 5 sequences with this command:

The labels with _1 and _2 represent paired-end files; mate1 is found in a file labelled _1 and mate2 is found in the file labelled _2. The files which do not have a number in their name are singled ended reads, this can be for two reasons, some sequencing early in the project was singled ended, also, as we filter our fastq files as described in our README if one of a pair of reads gets rejected the other read gets placed in the single file.

When a individual has many files with different run accessions (e.g ERR001268), this means it was sequenced multiple times. This can either be for the same experiment, some centres used multiplexing to have better control over their coverage levels for the low coverage sequencing, or because it was sequenced using different protocols or on different platforms.

Our files are named with the SRA run accession E?SRR000000.filt.fastq.gz. All the reads in the file also hold this name. The files with _1 and _2 in their names are associated with paired end sequencing runs. If there is also a file with no number it is name this represents the fragments where the other end failed qc. The .filt in the name represents the data in the file has been filtered after retrieval from the archive. This filtering process is described in a README.

This name starts with the population that the variants were discovered in, if ALL is specifed it means all the individuals available at that date were used. Then the region covered by the call set, this can be a chromosome, wgs (which means the file contains at least all the autosomes) or wex (this represents the whole exome) and a description of how the call set was produced or who produced it, the date matches the sequence and alignment freezes used to generate the variant call set. Next a field which describes what type of variant the file contains, then the analysis group used to generate the variant calls, this should be low coverage, exome or integrated and finally we have either sites or genotypes. A sites file just contains the first eight columns of the vcf format and the genotypes files contain individual genotype data as well.

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