add single observation */

* @brief Append a ReferenceNode to a haplotype block * * @param ht Haplotype block with end() < start */

Extract average depths + idxstats data from BAM file, return data frame

get the best possible score for comparing two sequences of length len */

Return the number of bases covered by intervals in chr:[start,end) :param chrom: Chromosome :param start: start (1-based) :pa

* Enumerate paths for a reference graph */

Return a data frame with features collected from the given VCF, tagged by given type :param vcfname: name of the VCF file :param tag: type of

Return a data frame with features collected from the given VCF, tagged by given type :param vcfname: name of the VCF file :param tag: type of

Return a data frame with features collected from the given VCF, tagged by given type :param vcfname: name of the VCF file :param tag: type of

extract field into result, guess type

Create ROC from feature table :param tbl: the table :type tbl: pandas.DataFrame :rtype: pandas.DataFrame

* @brief Retrieve an info field as a double * * @return the value or NaN */

get / create pool

make variants from a cigar string */

returns if list of chr names has a chr prefix or not

Returns true if ch is a control character (in range [0,32[).

* Initialize the query data structure * * @param size Number of bytes used to store a score; valid valures are 1 or 2 * @param qlen Length of t

* @brief Format int encoded Cigar string * * @param tb for padding with "S" : begin * @param te for padding with "S" : end * @param altlen for pad

* Create a diploid reference graph from a list of Variants records * Optionally, will return the number of unphased het variants * (proxy for number

Create PUMA metrics dictionary This is how they look like { name: 'PAM.GCbias', timestamp: 'Wed Sep 05 16:23:46 2012', ve

factory method */

merge two intervals

* Make RefVar */

reset / remove interval range */

* Reset / remove values up to and including position end * * @param end the end position, or -1 to use the smae as start */

set depth normalisation factors (can come from VCF or BAM)

* Set the comparison mode */

* Set comparison parameters * @param params parameters for the comparison method */

initialize acceptable assignments based on number of copies and haps */

* Enable / disable the alignment step to find the best approximately matching * haplotypes (i.e. stop after match/mismatch status have been establish

update format with single float values. */

* Set the maximum number of haplotype blocks to enumerate before aborting */

* Input sequences */

* Convert a list of RefVar records to allele strings */

Converts an unsigned integer to string. * @param value Unsigned interger to convert to string * @param current Input/Output string buffer. *

Add a BED entry to the tree :param bedentry: BED entry [chr, start, stop(, optional extra fields)] :type bedentry: list of int and st

turn a list of locations into a bed file

post-process ROC data by correcting the types, sorting and changing the ordering of the columns

Extract aligner information from a BAM file :param bamfile: name of BAM file

Add caller versions from a VCF :param vcfname: VCF file name

* @brief Advance one line * @return true if a variant was retrieved, false otherwise */

* @brief Advance one line * @return true if a variant was retrieved, false otherwise */

add Regions annotation to a record * * Records must be passed in sorted order. * */

* Append 1 char to RefVar. * * Return false if this is not possible because the variants are incompatible */

Check for overlaps / out of order in a bed file

Check if the python version is sufficient

Start and end of block */

Converts a unicode code-point to UTF-8.

* @brief Compare two variants * */

Concatenate BCF files Trickier than it sounds because when there are many files we might run into various limits like the number of open fi

* @brief Decompose a RefVar into primitive variants (subst / ins / del) by means of realigning * * @param f reference sequence fasta * @param chr t

Create a Python runtime environment :return: shebang with path to the python executable

Return a data frame with features collected from the given VCF, tagged by given type

Return a data frame with features collected from the given VCF, tagged by given type

Return a data frame with features collected from the given VCF, tagged by given type :param vcfname: name of the VCF file :param tag: type of

Return a data frame with features collected from the given VCF, tagged by given type

Return a data frame with features collected from the given VCF, tagged by given type

F1 score: https://en.wikipedia.org/wiki/F1_score */

Copies each line of :param:`input_stream` to :param:`output_stream` unless that line describes a variant where any of the sample genotypes