List of the software/R packages developed in An lab:

 

metaDprof:

Differential abundance analysis for time-course metagenomic sequencing count data

RAIDA: 

R package for robustly identifying differential abundant features across microbial conditions

ENNB:

A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes

metaFunction:

R package for statistical profiling functions in a microbial community

FunctionSIM:

(Java software) A sequencing simulator for functional metagenomics

TAEC:

R package for Taxonomic Analysis by Elimination and Correction on closely related species

TAMER:

R package for accurate taxonomic assignment of metagenomic sequencing reads

 


metaDprof:  differential abundance analysis for time-course metagenomic sequencing count data

A spline-based statistical approach, metaDprof, is developed to detect metagenomic features differentially abundant between biological/medical conditions. It consists two stages: 1) global detection of features and 2) time interval detection for significant features. This approach allows heterogeneous error/noise for different biological/medical conditions and no prior information is needed for the time interval detection. Even more, this method relies on sound statistical support for both detections.

 

Download R code.

Manual file

 

Example dataset

 

Citation: Luo D, Ziebell S and An L.  An Informative Approach on Differential Abundance Analysis for Time-course Metagenomic Sequencing Count Data. Bioinformatics, 2016 accepted.


RAIDA:  R package for robustly identifying differential abundant features across microbial conditions

RAIDA - Ratio Approach for Identifying Differential Abundance - is a robust approach for identifying differentially abundant features in metagenomic samples across different conditions. It utilizes the ratio between features in a modified zero-inflated lognormal model.

 

Download RAIDA package

 

Citation: Sohn M, Du R and An L.  A robust approach for identifying differentially abundant features in metagenomic samples. Bioinformatics, 2015 Mar 19. pii: btv165


 

ENNB: A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes:

 

In the first stage of the proposed procedure, the informative features are selected using Elastic Net as reducing the dimension of metagenomic data; in the second stage the differentially abundant features are detected using generalized linear models with a Negative Binomial distribution.

 

This is an introduction (README file) to using the R code for the proposed method for detection of significantly differentially abundant features of different metagenomic communities/conditions.

 

R code

 

Example data for two-group comparison (feature count and phenotype info)

Example data for multiple-group comparison (feature count and phenotype info)

 

Citation: Pookhao N, Sohn M, Li Q, Jenkins I, Du R, Jiang H, An L. (2015) A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes. Bioinformatics, 31:158-165.


metaFunction: A statistical tool in profiling functions in a microbial community

        Flowchart of metaFunction

Download the metaFunction package and the associated manual file, and the simulated sequence data (.fasta) in the metaFunction paper. 

More details can be found here.

Citation: An L, Pookhao N, Jiang H, Xu J. Statistical approach of functional profiling for a microbial community. PLoS ONE 2014, 9(9): e106588


FunctionSIM:  A sequencing simulator for functional metagenomics

 

As standalone software it allows users to simulate metagenomic sequence datasets that can be used as standardized test data for planning metagenomic projects or for benchmarking software in functional metagenomic analysis.

 

More details can be found at here. 


TAEC: an R package for Taxonomic Analysis by Elimination and Correction

TAEC, a new homology-based approach for taxonomic analysis, utilizes the similarity in the genomic sequence in addition to the result of an alignment tool.

This approach consists of two main stages: the elimination stage and the correction stage. In the elimination stage, the potential true genomes identified by removing false genomes whose presence is most likely due to the presence of similar genomes in a sample. In the correction stage, the abundances of the genomes remaining after the elimination stage are corrected by utilizing the similarity between genomes in a system of linear equations. The overall workflow of TAEC is shown as below.

 

Note:

¾      The light yellow colored blocks are implemented by a user and the light blue colored blocks are internally implemented by TAEC.

¾      The bacteria database could be replaced with virus or other types of databases if needed.

¾      Similarity matrix is given for different lengths of sequence reads, at 100pb, 250pb, 500pb, and 1000bp.

 

R package of TAEC can be downloaded: (Mac version) and (linux version)

 

Note:

We have tested the TAEC package on R version 2.14.1 (2011-12-22) and version 2.15.2 (2012-10-26) on Redhat and R version 3.0.2 (2013-09-25) on Ubuntu. No errors associated with the different versions of R occurred. On the other hand, we encountered errors associated with different versions of R on Mac OSX. In order for the TAEC package to work properly on OSX, please upgrade your R to the current version of 3.0.2 (2013-09-25).

 

Citation:  Sohn M, An L, Pookhao N, Li Q. Accurate genome relative abundance estimation for closely related species in a metagenomic sample. BMC Bioinformatics 2014, 15:242 .


 

TAMER: an R package for accurate taxonomic assignment of metagenomic sequencing reads.

 

This is a collaborative work with Hongmei Jiang’s lab.

More details can be found at here.

 

Citation: Jiang H *, An L*, Lin SM, Feng G, Qiu Y. A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads. PLoS ONE 7(10): e46450.  (*: co-first author)