Yes it is important you pre-process your NGS data before using read sequences in assembly or alignment. Examples of pre-processing include the removal of low-quality reads, trimming of poly-T tails and trimming of adapter sequences. Basically, you have to first carry out QC to know the quality of the raw data. To do this, you can try FastQC. Regardless of the reports from FastQC, it is a good idea to trim your raw reads before assembly. The primary reason for this is to remove poor quality reads that might reduce assembly speed and accuracy.

Although most assemblers have integrated error correction routines, filtering the reads will generally greatly reduce the time and memory overhead required for assembly, and probably improve results too. You could try any or a combination of these: cutadapt, FASTX and Trimmomatic.

Thank you, Oyediran for your quick reply.

I actually ran FastQC, and then I used SOAP de novo 2 for the assembly. But my Contig and Scaffold N50 values are too low so I have this question.

I thought about Trimmomatic, but I didn't run my data through Trimmomatic.

So are these softwares, Trimmomatic, FASTX, Cutadapt are used for larger genome, like human genome? And why do we need combination of any of these softwares, one won't do proper pre-processing?

What about other softwares, like QIIME, Pollux, Lotus, MG-RAST, Mothur, UPARSE, STAMPS, CloVR?

Thanks in advance,

FastQC result gives a brief summary of the data quality. Knowing this helps one to know what type of pre-processing would be needed. However, there is no rule that says one should use several of such tool. The quality of the data often dictates the type of pre-processing that would be needed. I have used Trimmomatic so I can recommend it. To the best of my understanding, the tools should work well irrespective of genome size. I have however not used any of the tools you mentioned before.

I presume you are working with microbial genome... Maybe you should consider trying other assemblers. You might find the decision helper here useful: https://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/De_novo_assembly

Thank you for your reply.

I am working with human genome, so it is a large genome to assemble de novo. SOAP de novo is one of the very good assembler for de novo assembly of large genome. All other assemblers I mentioned are also for preprocessing data but I am not sure how well they do with large genome. 

Could you please tell me how to assess what type of pre-processing would be needed from looking at the FastQC results of the data?

Thank you for the link, I am going through it now. Probably I will run Trimmomatic for preprocessing. Does it require coding in JAVA ?

Thank you again,

You might read this paper as well: ""Software for pre-processing Illumina next-generation sequencing short read sequences""  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4064128/

The second thing because you are using Human Genome, so you may try "The Genome Analysis Toolkit"  (GATK). It is a software package for analysis of high-throughput sequencing data, developed by the Data Science and Data Engineering group at the Broad Institute. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size.

Also this toolkit can be applied to all kinds of datasets and genome analysis problems. It's just as happy handling exomes as whole genomes. It can use data generated with a variety of different sequencing technologies. It can handle RNAseq data. And although it was originally developed for human genetics, the GATK has evolved to handle genome data from any organism, with any level of ploidy. 

Thank you for your suggestion and the reference paper. Yes, I am going to do pre-processing of my data, I thought before, but then I understood that most of the assemblers have that option for pre-processing, so I didn't do it, but now I realized I have to do it.

Thanks again,

Once you checked the quality of the libraries with FastQC you may consider using BBMap package, which I found very helpful, easy to use, moderate computing/memory footprint and a versatile tool to accomplish many related tasks e.g. trimming adapters, low-quality bases to merging or extending overlapping reads and many more.

http://seqanswers.com/forums/showthread.php?t=58221

http://sourceforge.net/projects/bbmap/