How to resolve KeyError: 'valmeanabsoluteerror' Keras 2.3.1 and TensorFlow 2.0 From Chollet Deep Learning with Python Ask Question Asked 1 year, 2 months ago. (OC) A deep dive on the asymmetrical 2-3-1-4, and how footedness affects spacing. 1) As I wrote when the lineups were announced, City once again played in an asymmetrical 2-3-1-4 formation when in possession, with Bernardo and Walker lining up deep either side of Gundogan, Mendy operating as a de facto left winger in attack, and Sterling. 2.3.1 Deeper is a tool with which allows you to easily configure the hidden functions of your Mac operating system. Download Deeper free and make your adjustments.
If you are not familiar with BAM, bedGraph and bigWig formats, you can read up on that in our Glossary of NGS terms
This tool takes an alignment of reads or fragments as input (BAM file) and generates a coverage track (bigWig or bedGraph) as output. The coverage is calculated as the number of reads per bin, where bins are short consecutive counting windows of a defined size. It is possible to extended the length of the reads to better reflect the actual fragment length. bamCoverage offers normalization by scaling factor, Reads Per Kilobase per Million mapped reads (RPKM), and 1x depth (reads per genome coverage, RPGC).
|--bam, -b||BAM file to process|
|Output file name.|
Output file type. Either “bigwig” or “bedgraph”.
Possible choices: bigwig, bedgraph
|Indicate a number that you would like to use. When used in combination with –normalizeTo1x or –normalizeUsingRPKM, the computed scaling factor will be multiplied by the given scale factor.|
|--MNase=False||Determine nucleosome positions from MNase-seq data. Only 3 nucleotides at the center of each fragment are counted. The fragment ends are defined by the two mate reads. Only fragment lengthsbetween 130 - 200 bp are considered to avoid dinucleosomes or other artifacts. By default, any fragments smaller or larger than this are ignored. To over-ride this, use the –minFragmentLength and –maxFragmentLength options, which will default to 130 and 200 if not otherwise specified in the presence of –MNase. *NOTE*: Requires paired-end data. A bin size of 1 is recommended.|
|--Offset||Uses this offset inside of each read as the signal. This is useful in cases like RiboSeq or GROseq, where the signal is 12, 15 or 0 bases past the start of the read. This can be paired with the –filterRNAstrand option. Note that negative values indicate offsets from the end of each read. A value of 1 indicates the first base of the alignment (taking alignment orientation into account). Likewise, a value of -1 is the last base of the alignment. An offset of 0 is not permitted.|
Selects RNA-seq reads (single-end or paired-end) in the given strand.
Possible choices: forward, reverse
|--version||show program’s version number and exit|
|Size of the bins, in bases, for the output of the bigwig/bedgraph file.|
|--region, -r||Region of the genome to limit the operation to - this is useful when testing parameters to reduce the computing time. The format is chr:start:end, for example –region chr10 or –region chr10:456700:891000.|
|A BED or GTF file containing regions that should be excluded from all analyses. Currently this works by rejecting genomic chunks that happen to overlap an entry. Consequently, for BAM files, if a read partially overlaps a blacklisted region or a fragment spans over it, then the read/fragment might still be considered. Please note that you should adjust the effective genome size, if relevant.|
|Number of processors to use. Type “max/2” to use half the maximum number of processors or “max” to use all available processors.|
|Set to see processing messages.|
|Report read coverage normalized to 1x sequencing depth (also known as Reads Per Genomic Content (RPGC)). Sequencing depth is defined as: (total number of mapped reads * fragment length) / effective genome size.The scaling factor used is the inverse of the sequencing depth computed for the sample to match the 1x coverage. To use this option, the effective genome size has to be indicated after the option. The effective genome size is the portion of the genome that is mappable. Large fractions of the genome are stretches of NNNN that should be discarded. Also, if repetitive regions were not included in the mapping of reads, the effective genome size needs to be adjusted accordingly. Common values are: mm9: 2,150,570,000; hg19:2,451,960,000; dm3:121,400,000 and ce10:93,260,000. See Table 2 of http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030377 or http://www.nature.com/nbt/journal/v27/n1/fig_tab/nbt.1518_T1.html for several effective genome sizes.|
|Use Reads Per Kilobase per Million reads to normalize the number of reads per bin. The formula is: RPKM (per bin) = number of reads per bin / ( number of mapped reads (in millions) * bin length (kb) ). Each read is considered independently,if you want to only count either of the mate pairs inpaired-end data, use the –samFlag option.|
|A list of space-delimited chromosome names containing those chromosomes that should be excluded for computing the normalization. This is useful when considering samples with unequal coverage across chromosomes, like male samples. An usage examples is –ignoreForNormalization chrX chrM.|
|This parameter determines if non-covered regions (regions without overlapping reads) in a BAM file should be skipped. The default is to treat those regions as having a value of zero. The decision to skip non-covered regions depends on the interpretation of the data. Non-covered regions may represent, for example, repetitive regions that should be skipped.|
|--smoothLength||The smooth length defines a window, larger than the binSize, to average the number of reads. For example, if the –binSize is set to 20 and the –smoothLength is set to 60, then, for each bin, the average of the bin and its left and right neighbors is considered. Any value smaller than –binSize will be ignored and no smoothing will be applied.|
|This parameter allows the extension of reads to fragment size. If set, each read is extended, without exception.*NOTE*: This feature is generally NOT recommended for spliced-read data, such as RNA-seq, as it would extend reads over skipped regions.*Single-end*: Requires a user specified value for the final fragment length. Reads that already exceed this fragment length will not be extended.*Paired-end*: Reads with mates are always extended to match the fragment size defined by the two read mates. Unmated reads, mate reads that map too far apart (>4x fragment length) or even map to different chromosomes are treated like single-end reads. The input of a fragment length value is optional. If no value is specified, it is estimated from the data (mean of the fragment size of all mate reads).|
|If set, reads that have the same orientation and start position will be considered only once. If reads are paired, the mate’s position also has to coincide to ignore a read.|
|If set, only reads that have a mapping quality score of at least this are considered.|
|By adding this option, reads are centered with respect to the fragment length. For paired-end data, the read is centered at the fragment length defined by the two ends of the fragment. For single-end data, the given fragment length is used. This option is useful to get a sharper signal around enriched regions.|
|Include reads based on the SAM flag. For example, to get only reads that are the first mate, use a flag of 64. This is useful to count properly paired reads only once, as otherwise the second mate will be also considered for the coverage.|
|Exclude reads based on the SAM flag. For example, to get only reads that map to the forward strand, use –samFlagExclude 16, where 16 is the SAM flag for reads that map to the reverse strand.|
|The minimum fragment length needed for read/pair inclusion. Note that a value other than 0 will exclude all single-end reads. This option is primarily useful in ATACseq experiments, for filtering mono- or di-nucleosome fragments.|
|The maximum fragment length needed for read/pair inclusion. A value of 0 disables filtering and is needed for including single-end and orphan reads.|
If you already normalized for GC bias using
correctGCbias, you should absolutely NOT set the parameter
If you know that your files will be strongly affected by the kind of filtering you would like to apply (e.g., removal of duplicates with
--ignoreDuplicates or ignoring reads of low quality) then consider removing those reads beforehand.
Like BAM files, bigWig files are compressed, binary files. If you would like to see the coverage values, choose the bedGraph output via
This is an example for ChIP-seq data using additional options (smaller bin size for higher resolution, normalizing coverage to 1x mouse genome size, excluding chromosome X during the normalization step, and extending reads):
If you had run the command with
--outFileFormatbedgraph, you could easily peak into the resulting file.
As you can see, each row corresponds to one region. If consecutive bins have the same number of reads overlapping, they will be merged.
Note that some BAM files are filtered based on SAM flags (Explain SAM flags).
Sometimes it makes sense to generate two independent bigWig files for all reads on the forward and reverse strand, respectively.As of deepTools version 2.2, one can simply use the
--filterRNAstrand option, such as
--filterRNAstrandreverse.This handles paired-end and single-end datasets. For older versions of deepTools, please see the instructions below.
--filterRNAstrand option assumes the sequencing library generated from ILLUMINA dUTP/NSR/NNSR methods, which are the most commonly used method forlibrary preparation, where Read 2 (R2) is in the direction of RNA strand (reverse-stranded library). However other methods exist, which generate readR1 in the direction of RNA strand (see this review). For these libraries,
--filterRNAstrand will have an opposite behavior, i.e.
--filterRNAstrandforward will give you reverse strand signal and vice-versa.
To follow the examples, you need to know that
-f will tell
samtoolsview to include reads with the indicated flag, while
-F will lead to the exclusion of reads with the respective flag.
For a stranded `single-end` library
For a stranded `paired-end` library
Now, this gets a bit cumbersome, but future releases of deepTools will make this more straight-forward.For now, bear with us and perhaps read up on SAM flags, e.g. here.
For paired-end samples, we assume that a proper pair should have the mates on opposing strands where the Illumina strand-specific protocol produces reads in a
R2-R1 orientation. We basically follow the recipe given in this biostars tutorial.
To get the file for transcripts that originated from the forward strand:
To get the file for transcripts that originated from the reverse strand:
|deepTools Galaxy.||code @ github.|