#CLIOR
Higher Recall in Metagenomic Sequence Classification Exploiting Overlapping Reads
In recent years the ability to sequence microbiomic samples has enable a number of applications in different fields like ecology, medicine and microbiology. One of the most important problems is to taxonomically classify the reads extracted from a metagenomic sample. Most of the methods currently available focus on the classification of individual reads. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. %Therefore, state-of-the-art methods are indeed capable of correctly classify only half of the reads in the sample. %Thus, how to achieve better performances in terms of overall quality of classification remains a largely unsolved problem. Here, we present CLIOR (CLassification Improvement with Overlapping Reads), a metagenomic classification method that exploits the information captured by the reads overlap graph of the input dataset in order to improve recall and f-measure. In fact, with CLIOR is possible to boost the performances of a state-of-the-art metagenomic classifiers by inferring and/or correcting the assignment of reads with missing or erroneous labeling. Results on simulated, and synthetic metagenomes show that CLIOR can improve the recall rate substantially, sometime doubling the recall, and also increases the precision on average by 8%. Experiments on real metagenomes confirm that CLIOR is able to assign many more reads and that the abundance ratios are in line with previous studies.
#Installation ##Download CLIOR CLIOR
##Compilation:
Open terminal and go to CLIOR/Release/ and then:
make all
#Algorithm Option In the following paragraph is described the input file's structure and the parameters available in the CLIOR algorithm.
##File accepted and structure
In the following paragraph is described the input file's structure.
CLIOR to execute correctly needs two kind of file, one in which reads are divided into groups and another in which reads are classified.
For groups file the structure is the following:
READ_IDENTIFICATION,GROUP
To obtain this file call MetaProb with -mg option, and if possible use paired-end files.
For classification file the structure is the following:
READ_IDENTIFICATION,Length,Class
READ_IDENTIFICATION,Length,Class (first read)
To obtain this file use Clark-l algorithm, or other software and convert the output in this format.
ATTENTION: First line is a identification line with 3 colums in that order
##Parameter
-si File paths of groups and classification for single-end dataset. Ex. -si pathgroups pathclass
-pi File paths of groups and classification for paired-end dataset. Ex. -pi pathgroups1 pathclass1 pathgroups2 pathclass2
-dirOutput Path directory output files. Default: output/
#Run CLIOR needs to compute groups and classification of read datasets in a certain structure of file (See file accepted and structure). You need to download two software which can do this without translate outputs and these are MetaProb and Clark.
Calls algorithm where is compiled:
./CLIOR -si pathfilegroups/example.groups.csv pathfileclass/example.csv
./CLIOR -pi pathfilegroups/example1.groups.csv pathfileclass/example1.csv pathfilegroups/example2.groups.csv pathfileclass/example2.csv
./CLIOR -pi pathfilegroups/example1.groups.csv pathfileclass/example1.csv pathfilegroups/example2.groups.csv pathfileclass/example2.csv
##Download MetaProb MetaProb
##Download Clark Clark
#Test File
Paired-End_split_dataset_part01
Paired-End_split_dataset_part02
Paired-End_split_dataset_part03
Paired-End_split_dataset_part04
Paired-End_split_dataset_part05
Paired-End_split_dataset_part06
Paired-End_split_dataset_part07
Paired-End_split_dataset_part08
Paired-End_split_dataset_part09
Paired-End_split_dataset_part10
HiSeq_MiSeq_Simba5_part01
HiSeq_MiSeq_Simba5_part02
HiSeq_MiSeq_Simba5_part03
HiSeq_MiSeq_Simba5_part04
#Publication
S.Girotto, M.Comin, C.Pizzi
Higher recall in metagenomic sequence classification exploiting overlapping reads
BMC Genomics 2017, 18 (Suppl 10) :917
DOI: https://doi.org/10.1186/s12864-017-4273-6
#License MIT