文件列表:

SAT (0, 2021-12-12)
SAT\RF_hexamer.R (2075, 2021-12-12)
SAT\featureEncoding.sh (1035, 2021-12-12)
SAT\gcForest_hexamer.py (14764, 2021-12-12)
SAT\generateBGSeq.py (503, 2021-12-12)
SAT\predict.py (2677, 2021-12-12)
SAT\translate.R (734, 2021-12-12)
Source_codes (0, 2021-12-12)
Source_codes\00_bam_to_sam.xml (769, 2021-12-12)
Source_codes\00_sam_to_bam.xml (770, 2021-12-12)
Source_codes\01_quality_contral.xml (2590, 2021-12-12)
Source_codes\02_trim_raw_reads.xml (7206, 2021-12-12)
Source_codes\03_extract_unmapped_reads.xml (6818, 2021-12-12)
Source_codes\04_remove_contamination.xml (11958, 2021-12-12)
Source_codes\05_assemble_unmapped_reads.xml (5130, 2021-12-12)
Source_codes\06_expression_level_evidence.xml (7073, 2021-12-12)
Source_codes\07_genome_level_evidence.xml (7037, 2021-12-12)
Source_codes\08_transcript_level_evidence.xml (4958, 2021-12-12)
Source_codes\09_protein_level_evidence.xml (3657, 2021-12-12)
Source_codes\10_SAT.xml (12402, 2021-12-12)
Source_codes\11_NAFeature.xml (4136, 2021-12-12)
Source_codes\12_AAFeature.xml (12850, 2021-12-12)
Source_codes\13_alternative_splicing.xml (1886, 2021-12-12)
Source_codes\14_condition_specific_analysis.xml (2900, 2021-12-12)
Source_codes\15_HeteogeneousAnalysis.xml (1817, 2021-12-12)
Source_codes\16_expression_and_DE.xml (6652, 2021-12-12)
Source_codes\17_co-expression_and_GO.xml (7783, 2021-12-12)
Source_codes\18_extract_sequence.xml (6379, 2021-12-12)
Source_codes\19_remove_batch_effect.xml (2068, 2021-12-12)
Supplementary_data (0, 2021-12-12)
Supplementary_data\Figure S1_PCR amplification and sequencing of eight randomly selected transcripts assembled using unmapped RNA-Seq reads from stripe rust-infected wheatinoculation.pdf (5398686, 2021-12-12)
Supplementary_data\Figure S2_PCR amplification and sequencing of eight randomly selected maize transcripts assembled using unmapped RNA-Seq reads from 171 B73 samples.pdf (4538195, 2021-12-12)
Supplementary_data\Supplementary Documents.docx (857479, 2021-12-12)
Supplementary_data\Supplementary Table S10_Primer sequences of eight maize transcripts used for experimental validation.xlsx (10472, 2021-12-12)
Supplementary_data\Supplementary Table S11_Differential expression and network module information of maize transcripts.xlsx (2138274, 2021-12-12)
Supplementary_data\Supplementary Table S12_GO enrichment results of each module in the co-expression network.xlsx (57521, 2021-12-12)
Supplementary_data\Supplementary Table S1_Selected list of RNA-Seq analysis pipelines.xlsx (23618, 2021-12-12)
Supplementary_data\Supplementary Table S2_Overview of functional modules in CAFU.xlsx (14715, 2021-12-12)
... ...

[](https://hub.docker.com/r/malab/cafu/) ## CAFU - CAFU is a Galaxy-based bioinformatics framework for comprehensive assembly and functional annotation of unmapped RNA-seq data from single- and mixed-species samples which integrates plenty of existing NGS analytical tools and our developed programs, and features an easy-to-use interface to manage, manipulate and most importantly, explore large-scale unmapped reads. - Besides the common process of reads cleansing, reads mapping, unmapped reads generation and novel transcription assembly, CAFU optionally offers the multiple-level evidence analysis of assembled transcripts, the sequence and expression characteristics of assembled transcripts, and the functional exploration of assembled transcripts through gene co-expression analysis and genome-wide association analysis. - Taking advantages of machine learning (ML) technologies, CAFU also effectively addresses the challenge of classifying species-specific transcripts assembled using unmapped reads from mixed-species samples. - The CAFU project is hosted on GitHub(https://github.com/cma2015/CAFU) and can be accessed from http://omicstudio.cloud:4001/. The CAFU Docker image is available at https://hub.docker.com/r/malab/cafu.  ## Overview of functional modules in CAFU - [**Extraction of unmapped reads**](https://github.com/cma2015/CAFU/blob/master/Tutorials/Extraction_mapped_reads.md) - [***De novo* transcript assembly of unmapped reads**](https://github.com/cma2015/CAFU/blob/master/Tutorials/De_novo_transcript_assembly_of_unmapped_reads.md) - [**Evidence support of assembled transcripts**](https://github.com/cma2015/CAFU/blob/master/Tutorials/Evidence_support_of_assembled_transcripts.md) - [**Species assignment of assembled transcripts**](https://github.com/cma2015/CAFU/blob/master/Tutorials/SAT.md) - [**Sequence characterization of assembled transcripts**](https://github.com/cma2015/CAFU/blob/master/Tutorials/Sequence%20characterization%20of%20assembled%20transcripts.md) - [**Expression profiles of assembled transcripts**](https://github.com/cma2015/CAFU/blob/master/Tutorials/Expression%20profiles%20of%20assembled%20transcripts.md) - [**Function annotation of assembled transcripts**](https://github.com/cma2015/CAFU/blob/master/Tutorials/Function%20annotation%20of%20assembled%20transcripts.md) ## How to use CAFU - Tutorials for CAFU: https://github.com/cma2015/CAFU/blob/master/Tutorials/User_manual.md - Test datasets referred in the tutorials for CAFU: https://github.com/cma2015/CAFU/tree/master/Test_data ## News and updates ### CAFU updated on Jan 1, 2019 - In the function **Assemble Unmapped Reads**, a parameter "Memory" was added for setting the maximum memory to be used by Triniry (1G in default). - To run the function **Species Assignment of Transcripts**, users can now use pre-trained or self-trained models. Currently, a pre-trained model was provided by training 20,502 and 137,052 mRNAs annotated in the reference genome of stripe rust pathogen *Puccinia striiformis f. sp. tritici* (PST-78 v1) and Chinese Spring wheat (IWGSC RefSeq v1.0), respectively. - The user tutorial was updated to highlight the importance of CPUs, Memory and Swap settings for running CAFU docker. ### CAFU updated on Nov 30, 2018 - A function **Remove Contamination** was added to remove potential contamination sequences using Deconseq (Schmieder *et al*., 2011). - A function **Remove Batch Effect** was added to remove batch effects using an R package sva (Leek *et al*., 2012). ### CAFU released on Oct 13, 2018 - CAFU source codes, web server and Docker image were released for the first time. ## How to access help * For any bugs/issues, please feel free to leave a message at Github [issues](). We will try our best to deal with all issues as soon as possible. ## How to cite this work Siyuan Chen, Chengzhi Ren, Jingjing Zhai, Jiantao Yu, Xuyang Zhao, Zelong Li, Ting Zhang, Wenlong Ma, Zhaoxue Han, Chuang Ma. CAFU: a Galaxy framework for exploring unmapped RNA-Seq data. *Briefings in Bioinformatics*, 2020;**21**:676-686.

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