Aliging sequences prior to phylip distance and

vmirly · March 29, 2015, 4:08pm

I want to apply clustering algorithm to the datasets from http://www.mothur.org/MiSeqDevelopmentData/

so lets consider one of the datasets (human dataset from 121203.tar). First, I performed a few preprocessing steps (pre.cluster, …), which remived the size of the data to 21575, and it looks like this:

>M00967_15_000000000-A2G1J_1_1101_16462_1801
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
>M00967_15_000000000-A2G1J_1_1101_16752_1822
TACGGAAGGTCCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGTATCGAACAGG
>M00967_15_000000000-A2G1J_1_1101_14896_1912

So, for the next step before computing the phylip distance matrix file, I have to align my sequences to some referenece datasets (SILVA or GreenGenes). I tried green-genes but mothur gave an error indicating that sequences in green-genes are not aligned. But when I tried SILVA, it worked!

So, my question is whether using SILVA vs. GreenGenes has different biological applications?

Here is the aligned data:

>M00967_15_000000000-A2G1J_1_2114_9838_26291
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...........................................................................................T-------AC---GG-AA-GGT---------------------
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----------------------------------------------------------------------------------------------------------------CCG-G-G---------------
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----------------------------------------------------------------------------------------------------C--G--T---T--AT-C-CGG--T------TT-A
--T-T--GG-GT--------------------------------------------------------------------------------------------------------------------------
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---------------------------------------------------------------------------------------------------------------------TT--A-----AA-GG-G
A-GC-------G-TA-G-G-C-C---------------G--G-AG-A-T-T-----------------------------------------------------------------------------------
----------------------------------------------------------------------------------------------AA----G-C-G-T---------------------------
--------------------------G-T-T--G--TG--A-AA-TG--T-A-GA-T-G---------------------------------------------------------------------------
-------------------------------------------------------------------CT-C-AA------------------------------------------------------------
----------------------------------------------------------------------------------------------------C-A-T-C-T-G-A--A-C----T-G--C-A---G
--C----------------------------G--CG-A-A---C----------------------------------------------------T--G-G--TT--T-C-C---------------------
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pschloss · March 30, 2015, 2:08pm

Not sure of the wording of your question, but we strongly discourage the use of greengenes for aligning sequences since it’s pretty shitty. It’s almost random within the variable region. Please save yourself some headache and use SILVA. You can see more about this here:

ncbi.nlm.nih.gov

A high-throughput DNA sequence aligner for microbial ecology studies.

PD Schloss, PloS one, Dec 2009 14

As the scope of microbial surveys expands with the parallel growth in sequencing capacity, a significant bottleneck in data analysis is the ability to generate a biologically meaningful multiple sequence alignment. The most commonly used aligners have varying alignment quality and speed, tend to depend on a specific reference alignment, or lack a complete description of the underlying algorithm. The purpose of this study was to create and validate an aligner with the goal of quickly generating a high quality alignment and having the flexibility to use any reference alignment. Using the simple nearest alignment space termination algorithm, the resulting aligner operates in linear time, requires a small memory footprint, and generates a high quality alignment. In addition, the alignments generated for variable regions were of as high a quality as the alignment of full-length sequences. As implemented, the method was able to align 18 full-length 16S rRNA gene sequences and 58 V2 region sequences per second to the 50,000-column SILVA reference alignment. Most importantly, the resulting alignments were of a quality equal to SILVA-generated alignments. The aligner described in this study will enable scientists to rapidly generate robust multiple sequences alignments that are implicitly based upon the predicted secondary structure of the 16S rRNA molecule. Furthermore, because the implementation is not connected to a specific database it is easy to generalize the method to reference alignments for any DNA sequence.

For classification, feel free to use SILVA, greengenes, or RDP.

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