PDF Combine 3.5 Torrent
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PDF Combine 3.5 Torrent
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To test the toy data set, you can also run the following command from the SPAdes bin directory: spades.py --pe1-1 ../share/spades/test_dataset/ecoli_1K_1.fq.gz \ --pe1-2 ../share/spades/test_dataset/ecoli_1K_2.fq.gz -o spades_test If you have your library separated into several pairs of files, for example: lib1_forward_1.fastq lib1_reverse_1.fastq lib1_forward_2.fastq lib1_reverse_2.fastq make sure that corresponding files are given in the same order: spades.py --pe1-1 lib1_forward_1.fastq --pe1-2 lib1_reverse_1.fastq \ --pe1-1 lib1_forward_2.fastq --pe1-2 lib1_reverse_2.fastq \ -o spades_output Files with interlacing paired-end reads or files with unpaired reads can be specified in any order with one file per option, for example: spades.py --pe1-12 lib1_1.fastq --pe1-12 lib1_2.fastq \ --pe1-s lib1_unpaired_1.fastq --pe1-s lib1_unpaired_2.fastq \ -o spades_output If you have several paired-end and mate-pair reads, for example: paired-end library 1 lib_pe1_left.fastq lib_pe1_right.fastq mate-pair library 1 lib_mp1_left.fastq lib_mp1_right.fastq mate-pair library 2 lib_mp2_left.fastq lib_mp2_right.fastq make sure that files corresponding to each library are grouped together: spades.py --pe1-1 lib_pe1_left.fastq --pe1-2 lib_pe1_right.fastq \ --mp1-1 lib_mp1_left.fastq --mp1-2 lib_mp1_right.fastq \ --mp2-1 lib_mp2_left.fastq --mp2-2 lib_mp2_right.fastq \ -o spades_output If you have IonTorrent unpaired reads, PacBio CLR and additional reliable contigs: it_reads.fastq pacbio_clr.fastq contigs.fasta run SPAdes with the following command: spades.py --iontorrent -s it_reads.fastq \ --pacbio pacbio_clr.fastq --trusted-contigs contigs.fastq \ -o spades_output If a single-read library is splitted into several files: unpaired1_1.fastq unpaired1_2.fastq unpaired1_3.fasta specify them as one library: spades.py --s1 unpaired1_1.fastq \ --s1 unpaired1_2.fastq --s1 unpaired1_3.fastq \ -o spades_output All options for specifying input data can be mixed if needed, but make sure that files for each library are grouped and files with left and right paired reads are listed in the same order.3.3 Assembling IonTorrent readsOnly FASTQ or BAM files are supported as input.The selection of k-mer length is non-trivial for IonTorrent. If the dataset is more or less conventional (good coverage, not high GC, etc), then use our recommendation for long reads (e.g. assemble using k-mer lengths 21,33,55,77,99,127). However, due to increased error rate some changes of k-mer lengths (e.g. selection of shorter ones) may be required. For example, if you ran SPAdes with k-mer lengths 21,33,55,77 and then decided to assemble the same data set using more iterations and larger values of K, you can run SPAdes once again specifying the same output folder and the following options: --restart-from k77 -k 21,33,55,77,99,127 --mismatch-correction -o . Do not forget to copy contigs and scaffolds from the previous run. We're planning to tackle issue of selecting k-mer lengths for IonTorrent reads in next versions. You may need no error correction for Hi-Q enzyme at all. However, we suggest trying to assemble your data with and without error correction and select the best variant. For non-trivial datasets (e.g. with high GC, low or uneven coverage) we suggest to enable single-cell mode (setting --sc option) and use k-mer lengths of 21,33,55.3.4 Assembling long Illumina paired reads (2x150 and 2x250)Recent advances in DNA sequencing technology have led to a rapid increase in read length. Nowadays, it is a common situation to have a data set consisting of 2x150 or 2x250 paired-end reads produced by Illumina MiSeq or HiSeq2500. However, the use of longer reads alone will not automatically improve assembly quality. An assembler that can properly take advantage of them is needed.SPAdes' use of iterative k-mer lengths allows benefiting from the full potential of the long paired-end reads. Currently one has to set the assembler options up manually, but we plan to incorporate automatic calculation of necessary options soon.Please note that in addition to the read length, the insert length also matters a lot. It is not recommended to sequence a 300bp fragment with a pair of 250bp reads. We suggest using 350-500 bp fragments with 2x150 reads and 550-700 bp fragments with 2x250 reads.Multi-cell data set with read length 2x150Do not turn off SPAdes error correction (BayesHammer module), which is included in SPAdes default pipeline.If you have enough coverage (50x+), then you may want to try to set k-mer lengths of 21, 33, 55, 77 (selected by default for reads with length 150bp).Make sure you run assembler with the --careful option to minimize number of mismatches in the final contigs.We recommend that you check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.For reads corrected prior to running the assembler: spades.py -k 21,33,55,77 --careful --only-assembler -o spades_output To correct and assemble the reads: spades.py -k 21,33,55,77 --careful -o spades_outputMulti-cell data set with read lengths 2 x 250Do not turn off SPAdes error correction (BayesHammer module), which is included in SPAdes default pipeline.By default we suggest to increase k-mer lengths in increments of 22 until the k-mer length reaches 127. The exact length of the k-mer depends on the coverage: k-mer length of 127 corresponds to 50x k-mer coverage and higher. For read length 250bp SPAdes automatically chooses K values equal to 21, 33, 55, 77, 99, 127.Make sure you run assembler with --careful option to minimize number of mismatches in the final contigs.We recommend you to check the SPAdes log file at the end of the each iteration to control the average coverage of the contigs.For reads corrected prior to running the assembler: spades.py -k 21,33,55,77,99,127 --careful --only-assembler -o spades_output To correct and assemble the reads: spades.py -k 21,33,55,77,99,127 --careful -o spades_outputSingle-cell data set with read lengths 2 x 150 or 2 x 250The default k-mer lengths are recommended. For single-cell data sets SPAdes selects k-mer sizes 21, 33 and 55.However, it might be tricky to fully utilize the advantages of long reads you have. Consider contacting us for more information and to discuss assembly strategy.3.5 SPAdes output SPAdes stores all output files in , which is set by the user. /corrected/ directory contains reads corrected by BayesHammer in *.fastq.gz files; if compression is disabled, reads are stored in uncompressed *.fastq files
/scaffolds.fasta contains resulting scaffolds (recommended for use as resulting sequences)
/contigs.fasta contains resulting contigs
/assembly_graph.gfa contains SPAdes assembly graph and scaffolds paths in GFA 1.0 format
/assembly_graph.fastg contains SPAdes assembly graph in FASTG format
/contigs.paths contains paths in the assembly graph corresponding to contigs.fasta (see details below)
/scaffolds.paths contains paths in the assembly graph corresponding to scaffolds.fasta (see details below)
Contigs/scaffolds names in SPAdes output FASTA files have the following format: >NODE_3_length_237403_cov_243.207_ID_45 Here 3 is the number of the contig/scaffold, 237403 is the sequence length in nucleotides and 243.207 is the k-mer coverage for the last (largest) k value used. Note that the k-mer coverage is always lower than the read (per-base) coverage. In general, SPAdes uses two techniques for joining contigs into scaffolds. First one relies on read pairs and tries to estimate the size of the gap separating contigs. The second one relies on the assembly graph: e.g. if two contigs are separated by a complex tandem repeat, that cannot be resolved exactly, contigs are joined into scaffold with a fixed gap size of 100 bp. Contigs produced by SPAdes do not contain N symbols. To view FASTG and GFA files we recommend to use Bandage visualization tool. Note that sequences stored in assembly_graph.fastg correspond to contigs before repeat resolution (edges of the assembly graph). Paths corresponding to contigs after repeat resolution (scaffolding) are stored in contigs.paths (scaffolds.paths) in the format accepted by Bandage (see Bandage wiki for details). The example is given below. Let the contig with the name NODE_5_length_100000_cov_215.651_ID_5 consist of the following edges of the assembly graph: >EDGE_2_length_33280_cov_199.702 >EDGE_5_length_84_cov_321.414' >EDGE_3_length_111_cov_175.304 >EDGE_5_length_84_cov_321.414' >EDGE_4_length_66661_cov_223.548 Then, contigs.paths will contain the following record: NODE_5_length_100000_cov_215.651_ID_5 2+,5-,3+,5-,4+ Since the current version of Bandage does not accept paths with gaps, paths corresponding contigs/scaffolds jumping over a gap in the assembly graph are splitted by semicolon at the gap positions. For example, the following record NODE_3_length_237403_cov_243.207_ID_45 21-,17-,15+,17-,16+; 31+,23-,22+,23-,4- states that NODE_3_length_237403_cov_243.207_ID_45 corresponds to the path with 10 edges, but jumps over a gap between edges EDGE_16_length_21503_cov_482.709 and EDGE_31_length_140767_cov_220.239.The full list of content is presented below:
CODESYS HMI SL is a software package that is installed on a remote computer to equip it with a graphical user interface (HMI). This new HMI support allows it to combine and visualize the data of one or more CODESYS V2 and CODESYS V3 controllers (data sources) on one remote HMI device. Since CODESYS V3.5 SP17 controllers with an OPC UA Server can also be visualized. A new datasource with OPC UA Client functionality is provided. The HMI SL can also be operated in conjunction with redundancy. An additional redudancy license is required for this.