Added support for STAR read alignment software

README.md

@@ -65,7 +65,7 @@ The workflow, configured to run Hisat2 and Stringtie, can then be launched by ru
 
         $ ./submit
 
-From here, the user may follow our documentation to modify the software options as well as point to their own input datasets.
 
 
 
@@ -96,8 +96,15 @@ file and gene annotation in GTF/GFF3 format, the following commands can be used
        $ tophat2 -G GRCh38.gencode.v24.annotation.gff3 --transcriptome-index=transcriptome_data/GRCh38 GRCh38
        $ tar czf GRCh38.transcriptome_data.tar.gz transcriptome_data/
        
+### If the user would like to use STAR:
+       # cd into the "star_index" directory within "reference".  Place all genome files here  
 
+       $ cd reference/star_index
+       $ STAR-2.5.2b/bin/Linux_x86_64_static/STAR --runMode genomeGenerate --runThreadN 4 --genomeDir ./ --genomeFastaFiles ./GRCh38.fa --sjdbGTFfile ./GRCh38.gencode.v24.annotation.gff3 
 
+#### Generate Tab delimited list of splice sites using gene model GTF file as input (Python DefaultDictionary Module necessary)
+
+       $ python hisat2_extract_splice_sites.py GRCh38-gencode.v24.annotation.gtf > GRCh38.Splice_Sites.txt
 
 
 ## Workflow Configuration
@@ -128,6 +135,19 @@ $REF_PREFIX.transcriptome_data.tar.gz
 
 $REF_PREFIX.gff3
 
+#### If the user would like to use, STAR, the following files must be present in the _reference/star_index_ directory:
+
+chrLength.txt
+chrNameLength.txt
+chrName.txt
+chrStart.txt
+Genome
+genomeParameters.txt
+Log.out
+SA
+SAindex
+*Splice_Sites.txt
+
 ### User Input Datasets
 
 OSG-GEM supports the processing of multiple input datasets into a single Gene Expression Matrix(GEM).  The user

osg-gem.conf.example

@@ -43,6 +43,9 @@ tophat2 = False
 # process using Hisat2
 hisat2 = True
 
+# process using STAR
+star = False
+
 # process using Cufflinks
 cufflinks = False
 

task-files/gem-merge/Log_merge.py

@@ -1,3 +1,4 @@
+
 ## William Poehlman ##
 ## LogParse.py v1.1 ##
 ## wpoehlm@clemson.edu ##
@@ -18,6 +19,7 @@ def ParseLogs(trimlist, maplist, software, sample, layout):
     maplist --> a list of hisat2 or tophat2 log files
     software --> read mapping software that generated log files.  'hisat' or 'tophat'
     sample --> A sample number that is present at the beginning of each filename
+    layout --> Sequencing layout.  'single' or 'paired'
 
     output:
     A dictionary, where the sample ID number is the key, and the following statistics are stored in the list value:
@@ -34,18 +36,22 @@ def ParseLogs(trimlist, maplist, software, sample, layout):
 
     ((left mapped reads + right mapped reads)/2)/(cleaned input pairs)
 
+    The overall Alignment rate for STAR is calculated as follows:
+    (uniquely mapped reads + multiple alignments) / (cleaned input pairs)
+
     Logfile names must be formatted as follows:
 
     <sample number>-<unique file identifier>-align_summary.txt (only for tophat)
     <sample number>-<unique file identifier>-fastq-out.txt (for hisat)
+    <sample number>-<unique file identifier>-Log.final.out
     <sample number>-unique file identifier>-fastq-trimmomatic.txt
 
     for example:
     2-000000-000000.fastq-align_summary.txt
     1-000000-000000.fastq-out.txt
     1-000000-000000.fastq-trimmomatic.txt
 
-    NOTE: do not mix log files for tophat and hisat
 
     """
     triminput = []
@@ -134,6 +140,91 @@ def ParseLogs(trimlist, maplist, software, sample, layout):
                 concordant.append(concordantpairs)
                 mapped.append(mappedsingles)
 
+    elif software == 'star' and layout == 'paired':
+        multimapped = []
+        uniqmapped = []
+        for file in trimlist:
+            basename = os.path.splitext(file)[0]
+            dataset = basename.split('-')[0]
+            if dataset == sample:
+                for line in open(file):
+                    if 'Input Read Pairs' in line:
+                        inp = int(line.split(' ')[3])
+                        triminput.append(inp)
+                        out = int(line.split(' ')[6])
+                        trimoutput.append(out)
+        for file in maplist:
+            basename = os.path.splitext(file)[0]
+            dataset = basename.split('-')[0]
+            if dataset == sample:
+                count = 0
+                for line in open(file):
+                    count +=1
+                    if count == 9:
+                        uniq = int(line.split('|')[1].strip())
+                        uniqmapped.append(uniq)
+                    if count == 24:
+                        multi = int(line.split('|')[1].strip())
+                        multimapped.append(multi)
+
+
+        concordant = sum(uniqmapped)
+        mult = sum(multimapped)
+        mapped = concordant + mult
+        inputraw = sum(triminput)
+        inputclean = sum(trimoutput)
+        float1 = float(mapped)
+        float2 = float(inputclean)
+        rate = float1/float2
+
+        report[sample] = [inputraw, inputclean, concordant, rate*100]
+
+        return report
+
+    if software == 'star' and layout == 'single':
+        multimapped = []
+        uniqmapped = []
+        for file in trimlist:
+            basename = os.path.splitext(file)[0]
+            dataset = basename.split('-')[0]
+            if dataset == sample:
+                for line in open(file):
+                    if 'Input Reads' in line:
+                        inp = int(line.split(' ')[2])
+                        triminput.append(inp)
+                        out = int(line.split(' ')[4])
+                        trimoutput.append(out)
+        for file in maplist:
+            basename = os.path.splitext(file)[0]
+            dataset = basename.split('-')[0]
+            if dataset == sample:
+                count = 0
+
+                for line in open(file):
+                    count +=1
+                    if count == 9:
+                        uniq = int(line.split('|')[1].strip())
+                        uniqmapped.append(uniq)
+                    if count == 24:
+                        multi = int(line.split('|')[1].strip())
+                        multimapped.append(multi)
+
+        concordant = sum(uniqmapped)
+        mult = sum(multimapped)
+        mapped = concordant + mult
+        inputraw = sum(triminput)
+        inputclean = sum(trimoutput)
+
+        float1 = float(mapped)
+        float2 = float(inputclean)
+        rate = float1/float2
+
+        report[sample] = [inputraw, inputclean, concordant, rate*100]
+
+        return report
+
+
+
     if software == 'tophat2' and layout == 'paired':
 
         inputraw = sum(triminput)
@@ -252,11 +343,12 @@ def main():
     #locate input log files and append to lists
 
     #tophat alignment summary
-    maploglist = [file for file in glob.glob(('*summary.txt'))]
-
-    if len(maploglist) == 0:
-        #If there are no tophat alignment summaries, look for hisat standard output instead
         maploglist = [file for file in glob.glob('*fastq-out.txt')]
+    elif software == 'star':
+        maploglist = [file for file in glob.glob('*Log.final.out')]
 
     trimloglist = [file for file in glob.glob('*-trimmomatic.txt')]
     if len(maploglist) != len(trimloglist):

task-files/star/fasta_adapter.txt

@@ -0,0 +1,130 @@
+>TRUSEQUNIVERSAL ADAPTER
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>TRUSEQADAPTER, INDEX 1
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACATCACGATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 2
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGATGTATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 3
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACTTAGGCATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 4
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 5
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACACAGTGATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 6
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGCCAATATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 7
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACCAGATCATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 8
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTTGAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 9
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGATCAGATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 10
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACTAGCTTATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 11
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGGCTACATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 12
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACCTTGTAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 13
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTCAACAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 14
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACAGTTCCGTATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 15
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACATGTCAGAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 16
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACCCGTCCCGATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 18 4
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTCCGCACATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 19
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGAAACGATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 20
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTGGCCTTATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 21
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGTTTCGGAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 22
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACCGTACGTAATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 23
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACGAGTGGATATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 25
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTGATATATCTCGTATGCCGTCTTCTGCTTG
+>TRUSEQADAPTER, INDEX 27
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACATTCCTTTATCTCGTATGCCGTCTTCTGCTTG
+>PE ADAPTER A
+GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG
+>PE ADAPTER B
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>PE PCR PRIMER 1.0
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>PE PCR PRIMER 2.0
+CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>PE READ 1 SEQUENCING PRIMER
+ACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>PE READ 2 SEQUENCING PRIMER
+CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCT
+>NEXTERA_CIRCULARIZED_DUPLICATE_JUNCTION_ADAPTER
+CTGTCTCTTATACACATCTAGATGTGTATAAGAGACAG
+>NEXTERA_CIRCULARIZED_SINGLE_JUNCTION_ADAPTER
+CTGTCTCTTATACACATCT
+>NEXTERA_CIRCULARIZED_SINGLE_JUNCTION_ADAPTER_REVERSE_COMPLEMENT
+AGATGTGTATAAGAGACAG
+>NEXTERA_READ_1_EXTERNAL_ADAPTER
+ATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>NEXTERA_READ_2_EXTERNAL_ADAPTER
+GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+>NEXTERA_CIRCULARIZED_DUPLICATE_JUNCTION_ADAPTER_REVERSE_COMPLEMENT
+CTGTCTCTTATACACATCTAGATGTGTATAAGAGACAG
+>NEXTERA_READ_1_EXTERNAL_ADAPTER_REVERSE_COMPLEMENT
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGAT
+>NEXTERA_READ_2_EXTERNAL_ADAPTER_REVERSE_COMPLEMENT
+ACACTCTTTCCCTACACGACGCTCTTCCGATC
+>GH_TBH_HINDIII_LEFT
+GTTGTAAAACGACGGCCAGTGAATTGTAATACGACTCACTATAGGGCGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCA
+>GH_TBH_HINDIII_RGHT
+AGCTTGAGTATTCTATAGTGTCACCTAAATAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAAC
+>GH_TBR_HINDIII_LEFT
+CTGAGCCTTTCGTTTTATTTGACCATGTTGGTATGATTTAAATTAATACGACTCACTATAGGGTCAGTGCGGCCGCGACTTCAAGTCACGTGAATTCCA
+>GH_TBR_HINDIII_RIGHT
+AGCTTCGGATCCCACTGTGGTGGATGCATTTTAACCCATCACATATACCTGCCGTTCACTATTATTTAGTGAAATGAGATATTATGATATTTTCTGAAT
+>GH_TBB_BAMHI_LEFT
+GGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTGTAATACGACTCACTATAGGGCGAATTCGAGCTCGGTACCCGGG
+>GH_TBB_BAMHI_RGHT
+GATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGAGTATTCTATAGTGTCACCTAAATAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGA
+>PCC2FOS_ECO27I_LEFT
+CGTTGTAAAACGACGGCCAGTGAATTGTAATACGACTCACTATAGGGCGAATTCGAGCTCGGTACCCGGGGATCCCACGTACAACGACACCTAGACCAC
+>PCC2FOS_ECO27I_RGHT
+GTGTTCCTAGGCTGTTTCCTGGTGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGAGTATTCTATAGTCTCACCTAAATAGCTTGGCGTAATC
+>GH_TBH_HINDIII_LEFT_RC
+TGCATGCCTGCAGGTCGACTCTAGAGGATCCCCGGGTACCGAGCTCGAATTCGCCCTATAGTGAGTCGTATTACAATTCACTGGCCGTCGTTTTACAAC
+>GH_TBH_HINDIII_RGHT_RC
+GTTGTGTGGAATTGTGAGCGGATAACAATTTCACACAGGAAACAGCTATGACCATGATTACGCCAAGCTATTTAGGTGACACTATAGAATACTCAAGCT
+>GH_TBR_HINDIII_LEFT_RC
+TGGAATTCACGTGACTTGAAGTCGCGGCCGCACTGACCCTATAGTGAGTCGTATTAATTTAAATCATACCAACATGGTCAAATAAAACGAAAGGCTCAG
+>GH_TBR_HINDIII_RIGHT_RC
+ATTCAGAAAATATCATAATATCTCATTTCACTAAATAATAGTGAACGGCAGGTATATGTGATGGGTTAAAATGCATCCACCACAGTGGGATCCGAAGCT
+>GH_TBB_BAMHI_LEFT_RC
+CCCGGGTACCGAGCTCGAATTCGCCCTATAGTGAGTCGTATTACAATTCACTGGCCGTCGTTTTACAACGTCGTGACTGGGAAAACCCTGGCGTTACCC
+>GH_TBB_BAMHI_RGHT_RC
+TCACACAGGAAACAGCTATGACCATGATTACGCCAAGCTATTTAGGTGACACTATAGAATACTCAAGCTTGCATGCCTGCAGGTCGACTCTAGAGGATC
+>PCC2FOS_ECO27I_LEFT_RC
+GTGGTCTAGGTGTCGTTGTACGTGGGATCCCCGGGTACCGAGCTCGAATTCGCCCTATAGTGAGTCGTATTACAATTCACTGGCCGTCGTTTTACAACG
+>PCC2FOS_ECO27I_RGHT_RC
+GATTACGCCAAGCTATTTAGGTGAGACTATAGAATACTCAAGCTTGCATGCCTGCAGGTCGACTCTAGAGGATCCCACCAGGAAACAGCCTAGGAACAC
+>CDJA
+CTGTCTCTTATACACATCTAGATGTGTATAAGAGACAG
+>CSJA
+CTGTCTCTTATACACATCT
+>CSJARC
+AGATGTGTATAAGAGACAG
+>R1EA
+GATCGGAAGAGCACACGTCTGAACTCCAGTCAC
+>R2EA
+GATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
+>PREFIXPE/1
+TACACTCTTTCCCTACACGACGCTCTTCCGATCT
+>PREFIXPE/2
+GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
+>INDEX_PRIMER_SEQUENCE
+CAAGCAGAAGACGGCATACGAGATATTGGCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATC
+>NEBNEXT_UNIVERSAL_FOR_ILLLUMINA
+AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATC
+>NEBNEXT_ADAPTER_ILLUMINA
+GATCGGAAGAGCACACGTCTGAACTCCAGTCACACTCTTTCCCTACACGA         

tools/StringTie

@@ -45,3 +45,4 @@ python ST_ttab_parse.py t_data.ctab > $BASE_NAME-merged_counts.fpkm
 mv t_data.ctab $BASE_NAME-t_data.ctab
 mv e_data.ctab $BASE_NAME-e_data.ctab
 mv i_data.ctab $BASE_NAME-i_data.ctab
+