Seq2Fun version 2 is an ultra-fast, all-in-one high resolution functional profiling tool directly from RNA-seq raw reads for organisms without reference genomes. For more detailed descriptions of the concept and algorithm, and instructions, please visit the Seq2Fun website www.seq2fun.ca.
If you want to use version 1 , please download the source code here and database from here
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Ultra-fast: > 120 times faster (~ 2 million reads / minute) than the conventional RNA-seq workflow.
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Extremely low memory cost: consumes as little as 2.27 GB memory and can run on a standard PC with 8 threads and 16 GB memory.
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Reference-free: does not require the genome or transcriptome reference of the organism; it is also transcriptome de novo assembly-free.
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All-in-one: Seq2Fun directly takes raw RNA-seq reads as input and output gene abundance table without any intermediate file writing and loading, making I/O very efficient.
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Multifunctional: Seq2Fun generates several output files, including ortholog gene bundance tables, a html report summarizing these tables and reads quality check, as well as output mapped clean reads for further analysis such as gene assembly.
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Flexible: supports RNA-seq analysis on particular genes or groups of organisms using customized database.
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Easy to use: requires minimal programing skills.
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Support target gene assemble: new features to extract mapped reads and conduct target gene assemble.
Seq2Fun (version 2.0.0) is written in C/C++11 and can be installed on Linux or Mac OS X (with Xcode and Xcode Command Line Tools installed). We have tested Seq2Fun on Ubuntu (16.04 LTS and above) and macOS Catalina.
git clone https://github.com/xia-lab/Seq2Fun.git
cd Seq2Fun/src/
make clean
make
For most non-model organisms, biological understanding of study outcomes is limited to protein-coding genes with functional annotations such as KEGG pathways, Gene Ontology or PANTHER classification system. Therefore, developing Seq2Fun version 2 database to focus on functionally annotated genes such as orthologs largely meets the preferred needs of most scientists studying non-model organisms.
We provide dozens of pre-built databases that can be downloaded from here.
There are four sub folders under seq2fun - src, bin, database and testdata. The bin folder contains the binary code we just complied. The testdata contains a small test data from the Case Study.
Download the birds database to the testdata folder, and issue the following commands:
tar -xzvf birds.tar.gz
From within the testdata folder, issue the following commands:
../bin/seq2fun --sampletable sample.txt --tfmi birds/birds.fmi --genemap birds/birds_annotation.txt -w 8 --profiling -V --outputMappedCleanReads --outputReadsAnnoMap
or if you want the trim the first 6 bases
../bin/seq2fun --sampletable sample.txt --tfmi birds/birds.fmi --genemap birds/birds_annotation.txt --trim_front1 6 --trim_front2 6 -w 8 --profiling -V --outputMappedCleanReads --outputReadsAnnoMap
This short tutorial below demonstrates how to run Seq2Fun. We use a RNA-seq dataset from a real non-model organism double-crested cormorant (DCCO), treated with ethinyl estradiol (EE2) as a show case.
DCCO embryos were exposed via egg injection to EE2, a synthetic estrogen that is the active substance in some forms of birth control. Livers were harvested after 14 days exposure and immediately frozen in liquid nitrogen for total RNA extraction. Total RNA was sent to Genome Quebec (Montreal, Quebec, Canada), to build sequencing library with TruSeq RNA Library Prep Kit (San Diego, California, United States) before submitted to Illumina NovaSeq 6000 (San Diego, California, United States) for 100 bp PE reads sequencing.
Each sample was subsampled with 5 million reads, just for demonstration purpose. The samples can be download from here
| Group | Chemicals (dose) | Number of samples | Number of reads |
|---|---|---|---|
| High | EE2 (31.9 mg/ml) | 5 | 25,000,000 |
| Medium | EE2 (2.3 mg/ml) | 5 | 25,000,000 |
| Control | DMSO | 5 | 25,000,000 |
| Group | Number of species | Number of proteins | Number of ortholog | Database name |
|---|---|---|---|---|
| Birds | 31 | 482,205 | 24,076 | birds.tar.gz |
This file consists of 4 columns and separated by '\t'. The first column is the prefix name of each sample, and the second is the forward reads file, the thrid column is the reverse reads file, and last one is the sample group info. If you have a single-end (SE) reads, remove the reverse reads (third) column. It looks like this:
A1.CE2-S1 A1.CE2-S1_R1.fastq.gz A1.CE2-S1_R1.fastq.gz control
A3.CE1-M2 A3.CE1-M2_R1.fastq.gz A3.CE1-M2_R1.fastq.gz middle
A4.CE1-H5 A4.CE1-H5_R1.fastq.gz A4.CE1-H5_R1.fastq.gz high
B1.CE2-S2 B1.CE2-S2_R1.fastq.gz B1.CE2-S2_R1.fastq.gz control
B3.CE1-M3 B3.CE1-M3_R1.fastq.gz B3.CE1-M3_R1.fastq.gz middle
C1.CE2-S3 C1.CE2-S3_R1.fastq.gz C1.CE2-S3_R1.fastq.gz control
C3.CE1-M4 C3.CE1-M4_R1.fastq.gz C3.CE1-M4_R1.fastq.gz middle
D1.CE2-S4 D1.CE2-S4_R1.fastq.gz D1.CE2-S4_R1.fastq.gz control
D3.CE1-M5 D3.CE1-M5_R1.fastq.gz D3.CE1-M5_R1.fastq.gz middle
E1.CE2-S5 E1.CE2-S5_R1.fastq.gz E1.CE2-S5_R1.fastq.gz control
E3.CE1-H1 E3.CE1-H1_R1.fastq.gz E3.CE1-H1_R1.fastq.gz high
F3.CE1-H2 F3.CE1-H2_R1.fastq.gz F3.CE1-H2_R1.fastq.gz high
G3.CE1-H3 G3.CE1-H3_R1.fastq.gz G3.CE1-H3_R1.fastq.gz high
H2.CE1-M1 H2.CE1-M1_R1.fastq.gz H2.CE1-M1_R1.fastq.gz middle
H3.CE1-H4 H3.CE1-H4_R1.fastq.gz H3.CE1-H4_R1.fastq.gz high
Seq2Fun has two output modes: comparative and profiling mode (default). The comparative mode produces only the ortholog abundance table, while the profiling mode produces 4 tables 1). ortholog abundance table for all samples, 2). ortholog abundance table for all samples submit to networkanalyst and 3). annotation file sumbit to networkanalyst for downstream analysis, and ortholog abundance table for each sample, 4). mapped clean reads file, and 5). a html report summarizing these tables.
S2F_HOME/bin/seq2fun --sampletable sample.txt --tfmi S2F_HOME/database/birds/birds.fmi --genemap S2F_HOME/database/birds/birds_annotation.txt -w 8 --profiling --outputMappedCleanReads
of if you want to trim the first 6 bases
S2F_HOME/bin/seq2fun --sampletable sample.txt --tfmi S2F_HOME/database/birds/birds.fmi --genemap S2F_HOME/database/birds/birds_annotation.txt --trim_front1 6 --trim_front2 6 -w 8 --profiling --outputMappedCleanReads
This table has sample names, meta data and s2fid, reads count (how many reads have assigned to the s2fid/ortholog), KO, GO, gene symbol and gene description separated by '\t'.
#NAME A1.CE2-S1 A3.CE1-M2 A4.CE1-H5 B1.CE2-S2 B3.CE1-M3 C1.CE2-S3 C3.CE1-M4 D1.CE2-S4 D3.CE1-M5 E1.CE2-S5 E3.CE1-H1 F3.CE1-H2 G3.CE1-H3 H2.CE1-M1 H3.CE1-H4 annotation
#CLASS:XX control middle high control middle control middle control middle control high high high middle high -
s2f_10 596 723 689 326 721 728 831 459 407 394 616 633 499 823 681 K13524|GO:0003824;GO:0003867;GO:0005739;GO:0008483;GO:0009448;GO:0009450;GO:0016740;GO:0030170;GO:0032144;GO:0034386;GO:0042135;GO:0042802;GO:0047298;GO:0048148;|ABAT|4-aminobutyrate aminotransferase
s2f_100 6 17 22 1 22 7 27 5 10 6 13 11 5 17 24 K04137|GO:0004930;GO:0004935;GO:0004937;GO:0005886;GO:0007165;GO:0007186;GO:0016020;GO:0016021;GO:0071875;|ADRA1D|adrenoceptor alpha 1d
s2f_1000 164 107 103 66 148 143 140 104 94 129 92 91 105 123 147 K00344|GO:0008270;GO:0016491|CRYZ|crystallin zeta
s2f_10000 39 52 56 51 77 46 68 45 58 42 72 77 61 79 60 K10105|GO:0006464;GO:0009249|LIPT1|lipoyltransferase 1
s2f_10001 156 323 283 211 324 134 457 214 439 202 368 362 335 309 259 K14565|GO:0001094;GO:0001650;GO:0005634;GO:0005654;GO:0005730;GO:0005732;GO:0005829;GO:0015030;GO:0030515;GO:0031428;GO:0032040;GO:0042254;GO:0048254;GO:0051117;GO:0070761;|NOP58|nop58 ribonucleoprotein
s2f_10002 46 79 83 54 85 46 101 63 122 39 76 90 90 89 48 K25166|GO:0008168;GO:0016740;GO:0032259;|METTL13|methyltransferase 13, eef1a lysine and n-terminal methyltransferase
s2f_10003 53 110 113 126 98 46 110 63 132 45 125 132 105 125 108 K05292|GO:0005737;GO:0016255;GO:0030182;GO:0031410;GO:0042765;GO:0051402;|PIGT|phosphatidylinositol glycan anchor biosynthesis class t
s2f_10004 43 84 77 85 71 57 81 72 117 58 64 93 87 77 82 K03256|GO:0005634;GO:0008033;GO:0030488;GO:0031515;GO:0080009;|TRMT6|trna methyltransferase 6 non-catalytic subunit
s2f_10005 58 92 96 281 83 57 79 63 86 51 124 119 92 93 112 K02144|GO:0000221;GO:0006811;GO:0046961;GO:1902600;|ATP6V1H|atpase h+ transporting v1 subunit h
s2f_10006 123 167 174 149 185 87 167 119 149 72 196 196 143 179 198 K23387|GO:0045048|GET4|guided entry of tail-anchored proteins factor 4
s2f_10007 28 67 45 35 51 35 52 47 49 34 60 53 74 50 50 K00586|GO:0004164;GO:0008168;GO:0016740;GO:0017183;GO:0032259;|DPH5|diphthamide biosynthesis 5
s2f_10008 125 124 118 149 143 100 177 127 180 91 130 159 147 136 122 K20367|GO:0005783;GO:0006888;GO:0006890;GO:0016020;GO:0016021;GO:0016192;GO:0030134;GO:0030173;GO:0030176;GO:0033116;|ERGIC3|ergic and golgi 3
s2f_10009 14 37 47 46 44 21 52 48 53 26 38 48 39 35 43 K14535|GO:0005634;GO:0006352;GO:0046982|TAF9B|tata-box binding protein associated factor 9b
s2f_1001 110 226 244 674 228 108 223 218 461 159 216 277 353 180 205 K01647|GO:0005759;GO:0016740;GO:0046912|CS|citrate synthase
s2f_10010 1 3 3 34 5 1 2 3 30 1 4 8 8 1 2 U|GO:0007212;GO:0016020;GO:0016021;GO:0032051;GO:0048268;|NSG2|neuronal vesicle trafficking associated 2
s2f_10012 1 1 3 3 2 2 5 0 1 2 1 1 4 2 2 K09208|GO:0000978;GO:0000981;GO:0001228;GO:0003677;GO:0006357;GO:0008285;GO:0045647;GO:0045944;GO:1990837;|KLF13|kruppel like factor 13
s2f_10013 95 138 127 141 138 106 129 93 142 95 173 139 157 121 133 U|GO:0005764;GO:0005765;GO:0016020;GO:0016192;GO:0035658;GO:0043231;|CCZ1|ccz1 homolog, vacuolar protein trafficking and biogenesis associated
s2f_10014 5 11 17 36 13 10 18 14 30 12 17 28 27 20 15 K10417|GO:0005737;GO:0005813;GO:0005815;GO:0005856;GO:0005868;GO:0005874;GO:0005929;GO:0005930;GO:0007368;GO:0031514;GO:0035721;GO:0035735;GO:0035869;GO:0036064;GO:0045177;GO:0045504;GO:0060271;GO:1902017;|DYNC2LI1|dynein cytoplasmic 2 light intermediate chain 1
s2f_10015 197 248 217 197 207 173 233 173 193 168 225 238 218 216 176 K15119|GO:0016020;GO:0016021;GO:0055085;|SLC25A39|solute carrier family 25 member 39
s2f_10017 18 37 55 34 48 24 49 36 67 20 50 44 55 57 48 K18342|GO:0004843;GO:0006508;GO:0008233;GO:0008234|OTUD6B|otu K23387|GO:0045048|GET4|guided entry of tail-anchored proteins fact
...
This table has three columns separated by '\t', s2f_id/ortholog, reads_count(how many reads mapped to the s2f_id) and annotation.
#s2f_id Reads_cout annotation
s2f_10 596 K13524|GO:0003824;GO:0003867;GO:0005739;GO:0008483;GO:0009448;GO:0009450;GO:0016740;GO:0030170;GO:0032144;GO:0034386;GO:0042135;GO:0042802;GO:0047298;GO:0048148;|ABAT|4-aminobutyrate aminotransferase
s2f_100 6 K04137|GO:0004930;GO:0004935;GO:0004937;GO:0005886;GO:0007165;GO:0007186;GO:0016020;GO:0016021;GO:0071875;|ADRA1D|adrenoceptor alpha 1d
s2f_1000 164 K00344|GO:0008270;GO:0016491|CRYZ|crystallin zeta
s2f_10000 39 K10105|GO:0006464;GO:0009249|LIPT1|lipoyltransferase 1
s2f_10001 156 K14565|GO:0001094;GO:0001650;GO:0005634;GO:0005654;GO:0005730;GO:0005732;GO:0005829;GO:0015030;GO:0030515;GO:0031428;GO:0032040;GO:0042254;GO:0048254;GO:0051117;GO:0070761;|NOP58|nop58 ribonucleoprotein
s2f_10002 46 K25166|GO:0008168;GO:0016740;GO:0032259;|METTL13|methyltransferase 13, eef1a lysine and n-terminal methyltransferase
s2f_10003 53 K05292|GO:0005737;GO:0016255;GO:0030182;GO:0031410;GO:0042765;GO:0051402;|PIGT|phosphatidylinositol glycan anchor biosynthesis class t
s2f_10004 43 K03256|GO:0005634;GO:0008033;GO:0030488;GO:0031515;GO:0080009;|TRMT6|trna methyltransferase 6 non-catalytic subunit
s2f_10005 58 K02144|GO:0000221;GO:0006811;GO:0046961;GO:1902600;|ATP6V1H|atpase h+ transporting v1 subunit h
s2f_10006 123 K23387|GO:0045048|GET4|guided entry of tail-anchored proteins factor 4
... ... ...
each read was tagged with mapped s2f_id/ortholog
@A00266:275:HLFTWDSXX:2:2562:20925:30405 1:N:0:TCGGATTC+CGCAACTA s2f_3808
CCCGGATAATGAGTGGAACGGAGCAGATGGTGAAGAGGACGGTCATGAGCCCCAGCAGGATGAGGTGGTCGAGCTCCTCCATGCGAGGGGCAGCGGTGGCA
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
@A00266:275:HLFTWDSXX:2:1647:32579:6057 1:N:0:TCGGATTC+CGCAACTA s2f_2261
GTCTGTGACATCCTTTGCCTCAATGTGGCTAGGGGCATCAAGCTTTGTGGTTATCACTCTGGATAGTCCTGGTCCTCTGGTATTGTTTTTCACAATATGAA
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFF:FFFFF
@A00266:275:HLFTWDSXX:2:2608:13711:33098 1:N:0:TCGGATTC+CGCAACTA s2f_141
CCAGGAGGAAGCCGTTCTTGCAGGGAGGGGAGTCGAGGTTGTTCTCAATCAGCTCCACCACCATCTCATCACTCACCAGCTTCCCCGCATCCATCGTCTCC
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FF,FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF
@A00266:275:HLFTWDSXX:2:1327:17996:28714 1:N:0:TCGGATTC+CGCAACTA s2f_1351
AGCGCAGCACCGAGTAGCTGTTCTCATCGACCTTCTTGCTGTAGGTGACCTCGTACTTCCAGACCCGGCTCTGCTGCCGCGGTGGCACCGTCCAGGAGAGG
+
FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF:FFFFFFF
... ... ...
Use seq2fun or seq2fun --help to show the full usage options
options:
// input/output
-s, --sampletable, (recommended) sample table must consist of 3 columns (sample prefix name (sample01), forward reads name (sample01_R1.fq.gz), group info (control) for single-reads or 4 columns (sample prefix name (sample01), forward reads (sample01_R1.fq.gz), reverse reads (sample01_R2.fq.gz), group info (control) for paired-end reads. The columns must be separated by tab
-i, --in1 read1 input file name
-I, --in2 read2 input file name
-X, --prefix (not recommended) prefix name for output files, eg: sample01
--outputMappedCleanReads, enable output mapped clean reads into fastq.gz files, by default is false, using --outputMappedCleanReads to enable it
--outputReadsAnnoMap enable output mapped clean reads-annotation map into .gz files, by default is false, using --outputReadsAnnoMap to enable it
// Homology search;
-D, --genemap gene/protein species map
--profiling by default it is off. If this option is specified, several output files will be generated, s2f_id/ortholog abundance table.
// translated search
-d, --tfmi fmi index of Protein database
-K, --mode searching mode either tGREEDY or tMEM (maximum exactly match). By default greedy
-E, --mismatch number of mismatched amino acid in sequence comparison with protein database with default value 2
-j, --minscore minimum matching score of amino acid sequence in comparison with protein database with default value 100
-J, --minlength minimum matching length of amino acid sequence in comparison with protein database with default value 25, for GREEDY and MEM model
-m, --maxtranslength maximum cutoff of translated peptides, it must be no less than minlength, with default 60
--allFragments enable this function will force Seq2Fun to use all the translated AA fragments with length > minlength. This will slightly help to classify reads contain the true stop codon and start codon; This could have limited impact on the accuracy for comparative study and enable this function will slow down the Seq2Fun. by default is false, using --allFragments to enable it
--codontable select the codon table (same as blastx in NCBI), we provide 21 codon tables from 'https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi#SG31'. By default is the codontable1 (Standard Code), the complete codon table can be seen below.
--dbDir specify dir for internal databases such as ko_fullname.txt".
//selected pathways
-Z, --pathway list of selected pathways for target pathways analysis
-z, --genefa the gene/protein sequences fasta file for retrieving proteins in selected pathways to construct database
// threading
-w, --thread worker thread number, default is 2
-V, --verbose enable verbose
--debug enable debug
--phred64 indicate the input is using phred64 scoring (it'll be converted to phred33, so the output will still be phred33)
--reads_to_process specify how many reads/pairs to be processed. Default 0 means process all reads
// adapter
-A, --disable_adapter_trimming adapter trimming is enabled by default. If this option is specified, adapter trimming is disabled
-a, --adapter_sequence the adapter for read1. For SE data, if not specified, the adapter will be auto-detected. For PE data, this is used if R1/R2 are found not overlapped
--adapter_sequence_r2 the adapter for read2 (PE data only). This is used if R1/R2 are found not overlapped. If not specified, it will be the same as adapter_sequence
--adapter_fasta specify a FASTA file to trim both read1 and read2 (if PE) by all the sequences in this FASTA file
--detect_adapter_for_pe by default, the auto-detection for adapter is for SE data input only, turn on this option to enable it for PE data
//polyA tail
--no_trim_polyA by default, ployA tail will be trimmed. If this option is specified, polyA trimming is disabled
// trimming
-f, --trim_front1 trimming how many bases in front for read1, default is 0
-t, --trim_tail1 trimming how many bases in tail for read1, default is 0
-b, --max_len1 if read1 is longer than max_len1, then trim read1 at its tail to make it as long as max_len1. Default 0 means no limitation
-F, --trim_front2 trimming how many bases in front for read2. If it's not specified, it will follow read1's settings
-T, --trim_tail2 trimming how many bases in tail for read2. If it's not specified, it will follow read1's settings
-B, --max_len2 if read2 is longer than max_len2, then trim read2 at its tail to make it as long as max_len2. Default 0 means no limitation. If it's not specified, it will follow read1's settings
// polyG tail trimming
-g, --trim_poly_g force polyG tail trimming, by default trimming is automatically enabled for Illumina NextSeq/NovaSeq data
--poly_g_min_len the minimum length to detect polyG in the read tail. 10 by default
-G, --disable_trim_poly_g disable polyG tail trimming, by default trimming is automatically enabled for Illumina NextSeq/NovaSeq data
// polyX tail trimming
-x, --trim_poly_x enable polyX trimming in 3' ends
--poly_x_min_len the minimum length to detect polyX in the read tail. 10 by default
// cutting by quality
--cut_front move a sliding window from front (5') to tail, drop the bases in the window if its mean quality < threshold, stop otherwise
--cut_tail move a sliding window from tail (3') to front, drop the bases in the window if its mean quality < threshold, stop otherwise
-r, --cut_right move a sliding window from front to tail, if meet one window with mean quality < threshold, drop the bases in the window and the right part, and then stop
-W, --cut_window_size the window size option shared by cut_front, cut_tail or cut_sliding. Range: 1~1000, default: 4
-M, --cut_mean_quality the mean quality requirement option shared by cut_front, cut_tail or cut_sliding. Range: 1~36 default: 20 (Q20)
--cut_front_window_size the window size option of cut_front, default to cut_window_size if not specified
--cut_front_mean_quality the mean quality requirement option for cut_front, default to cut_mean_quality if not specified
--cut_tail_window_size the window size option of cut_tail, default to cut_window_size if not specified
--cut_tail_mean_quality the mean quality requirement option for cut_tail, default to cut_mean_quality if not specified
--cut_right_window_size the window size option of cut_right, default to cut_window_size if not specified
--cut_right_mean_quality the mean quality requirement option for cut_right, default to cut_mean_quality if not specified
// quality filtering
-Q, --disable_quality_filtering quality filtering is enabled by default. If this option is specified, quality filtering is disabled
-q, --qualified_quality_phred the quality value that a base is qualified. Default 15 means phred quality <=Q15 is qualified
-u, --unqualified_percent_limit how many percents of bases are allowed to be unqualified (0~100). Default 40 means 40%
-n, --n_base_limit if one read's number of N base is >n_base_limit, then this read/pair is discarded. Default is 5
-e, --average_qual if one read's average quality score <avg_qual, then this read/pair is discarded. Default 0 means no requirement
// length filtering
-L, --disable_length_filtering length filtering is enabled by default. If this option is specified, length filtering is disabled
-l, --length_required reads shorter than length_required will be discarded, default is 60
--length_limit reads longer than length_limit will be discarded, default 0 means no limitation
// low complexity filtering
--no_low_complexity_filter disable low complexity filter. The complexity is defined as the percentage of base that is different from its next base (base[i] != base[i+1])
-Y, --complexity_threshold the threshold for low complexity filter (0~100). Default is 30, which means 30% complexity is required
// filter by indexes
--filter_by_index1 specify a file contains a list of barcodes of index1 to be filtered out, one barcode per line
--filter_by_index2 specify a file contains a list of barcodes of index2 to be filtered out, one barcode per line
--filter_by_index_threshold the allowed difference of index barcode for index filtering, default 0 means completely identical
// base correction in overlapped regions of paired end data
-c, --disable_correction disenable base correction in overlapped regions (only for PE data), default is enabled");
-v, --overlap_len_require the minimum length to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. 30 by default
--overlap_diff_limit the maximum number of mismatched bases to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. 5 by default
--overlap_diff_percent_limit the maximum percentage of mismatched bases to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. Default 20 means 20%
// umi
-u, --umi enable unique molecular identifier (UMI) preprocessing
--umi_loc specify the location of UMI, can be (index1/index2/read1/read2/per_index/per_read, default is none
--umi_len if the UMI is in read1/read2, its length should be provided
--umi_prefix if specified, an underline will be used to connect prefix and UMI (i.e. prefix=UMI, UMI=AATTCG, final=UMI_AATTCG). No prefix by default
--umi_skip if the UMI is in read1/read2, Seq2Fun can skip several bases following UMI, default is 0
// overrepresented sequence analysis
-p, --overrepresentation_analysis enable overrepresented sequence analysis
-P, --overrepresentation_sampling one in (--overrepresentation_sampling) reads will be computed for overrepresentation analysis (1~10000), smaller is slower, default is 20
// deprecated options
--cut_by_quality5 DEPRECATED, use --cut_front instead
--cut_by_quality3 DEPRECATED, use --cut_tail instead
--cut_by_quality_aggressive DEPRECATED, use --cut_right instead
--discard_unmerged DEPRECATED, no effect now, see the introduction for merging
We have followed the codon tables from NCBI;
| Seq2Fun | NCBI |
|---|---|
| codontable1 | The Standard Code (transl_table=1) |
| codontable2 | The Vertebrate Mitochondrial Code (transl_table=2) |
| codontable3 | The Yeast Mitochondrial Code (transl_table=3) |
| codontable4 | The Mold, Protozoan, and Coelenterate Mitochondrial Code and the Mycoplasma/Spiroplasma Code (transl_table=4) |
| codontable5 | The Invertebrate Mitochondrial Code (transl_table=5) |
| codontable6 | The Ciliate, Dasycladacean and Hexamita Nuclear Code (transl_table=6) |
| codontable9 | The Echinoderm and Flatworm Mitochondrial Code (transl_table=9) |
| codontable10 | The Euplotid Nuclear Code (transl_table=10) |
| codontable12 | The Alternative Yeast Nuclear Code (transl_table=12) |
| codontable13 | The Ascidian Mitochondrial Code (transl_table=13) |
| codontable14 | The Alternative Flatworm Mitochondrial Code (transl_table=14) |
| codontable16 | Chlorophycean Mitochondrial Code (transl_table=16) |
| codontable21 | Trematode Mitochondrial Code (transl_table=21) |
| codontable22 | Scenedesmus obliquus Mitochondrial Code (transl_table=22) |
| codontable24 | Rhabdopleuridae Mitochondrial Code (transl_table=24) |
| codontable26 | Pachysolen tannophilus Nuclear Code (transl_table=26) |
| codontable27 | Karyorelict Nuclear Code (transl_table=27) |
| codontable29 | Mesodinium Nuclear Code (transl_table=29) |
| codontable30 | Peritrich Nuclear Code (transl_table=30) |
| codontable31 | Blastocrithidia Nuclear Code (transl_table=31) |
| codontable33 | Cephalodiscidae Mitochondrial UAA-Tyr Code (transl_table=33) |
To inform us of any bugs or requests, please open a new issue or send an email to rocpengliu@gmail.com or jeff.xia@mcgill.ca
03-16-2022 - seq2fun_v2.0.4 released(database with orthologs)
12-21-2021 - seq2fun_v2.0.0 released
08-23-2021 - seq2fun_v1.2.4 released
06-18-2021 - seq2fun_v1.2.3 released
06-05-2021 - seq2fun_v1.2.2 released
03-31-2021 - seq2fun_v1.2.1 released
03-26-2021 - seq2fun_v1.2.0 released
03-22-2021 - seq2fun_v1.1.4 released
01-14-2021 - seq2fun_v1.1.2 released
12-06-2020 - seq2fun_v1.1.0 released
08-24-2020 - seq2fun_v1.0.0 released