Is there any algorithmic approach to find the adapter traces within the reads efficiently.
The FASTX toolkit has fastx-clipper for removing adaptor sequences.
Take care with fastx-clipper. It was designed for small RNA experiments and is usually too greedy finding adaptor sequences with other experiments. Cutadapt seems to be a good alternative.
FASTQC (Babraham Inst; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) will give you an initial impression of whether adapter contamination is a problem or not.
I can also recommend cutadapt (http://code.google.com/p/cutadapt/). you can specify, where to look for adaptor sequences and even wildcards can be set within the adaptor.
Here is a seqanswer thread on adapter trimming: http://seqanswers.com/forums/showthread.php?t=5604
I agree with Bernat Gel response. Cutadapt (http://code.google.com/p/cutadapt/) is quite efficient on large datasets and provides better results as compared to fastx-clipper that appears less specific on our dataset ...
Cutadapt works pretty well. Before you can use FASTQC in order to get a good estimation of the quality of your data set.
Sequence cleaning through Blast-based searches using general and custom made vector/adaptor databases, such as the one implemented in SeqTrimNext pipeline (http://www.scbi.uma.es/portal/index.php?option=com_content&view=article&id=81:seqtrimnext&catid=25:tools&Itemid=60), can be used as an alternative or as a complement to the widely known cutadapt analysis. Apart from vector/adaptor trimming, It also performs some other useful preprocessing tasks as low quality or low complexity regions and contaminant sequences removal.
Have a look at FLEXBAR at:
http://bbc.mdc-berlin.de/software/flexbar_v2.21_linux64.tgz/view
I can recommend this.
From what you have asked, it seems that you are looking for adapter contamination. Many of the comments are related to adatper trimmin, which can report how many times partial or full adapter are found in the reads, e.g. cutadapt. I am not sure whether that is what you are looking for. Tools like FastQC or Bioconductor's ShortRead package might be helpful to quickly detect over-represented sequences in your reads and you can compare those reads to the adapter sequences. You can use 1% of the reads to get a sense of adapter contamination if you have a lot of samples to process and speed becomes an issue.
Two useful programs are:
FastQC: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Tagdust: http://www.ncbi.nlm.nih.gov/pubmed/19737799
NGS QC toolkit can be another good option, which can be find here: http://59.163.192.90:8080/ngsqctoolkit/. Best part is this tool is multi threaded .
Yet another adapter trimmer >>>
skewer: http://sourceforge.net/projects/skewer/files/Binaries/
which is fast and has the best performance according to the number of uniquely mapped reads after trimming.
The algorithm has been published in BMC Bioinformatics.
http://www.biomedcentral.com/1471-2105/15/182
you can analyze your data in "fast QC" tool which is provided by the server "useGalaxy" (https://usegalaxy.org/) it will tell you which type of adapter contamination your data contain.you can remove your adapter contamination using "Trim galore! " tool, either by automatic detection or you can provide type of adapter contamination.
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