Update: May 6, 2018: The scrubbing pipeline now begins with DAScover and the critical -c, -g, and -b parameters are estimated by the modules themselves (although the auto-selected values can be overridden). A new extension to the track mechanism allows the routine to pass values forward in the tracks, so that parameters need only be set once, e.g. the -H option for HGAP processing need only be specified to DAScover at the start and then it is propagated to all downstream phases of the scrubber. Finally, a module REPcover has been added analogous to REPqv and REPtrim, and the new @-shorthand is implemented for all scrubber commands that take lists of .las files.
Update: February 9, 2018: A new command, DAScover, can be run at the start of scrubbing or during data generation to estimate the coverage of the target genome. REPcover can be run at any time to give the statistics over the entire data base or selected blocks.
Update: August 17, 2017: Commands REPqv and REPtrim give the -v output of DASqv and DAStrim at any time after they have been run by directly examining the tracks produced. They also conveniently give you these statistics for the entire data base.
The latest source code can be found on Github here. This is still a preliminary release — the current set of commands provide a pipeline that one can use to scrub reads and if desired to scrub the alignment piles (with DASrealign). Ultimately DASpatch/DASedit and DASrealign will be replaced with more powerful programs that correct reads and not only scrub alignment piles, but also remove haplotype and repeat induced overlaps, prior to assembly via a string graph method.
The current “DAS” suite consists of a pipeline of several programs that in sequence accomplish the task of scrubbing: DAScover → DASqv → DAStrim → DASpatch → DASedit. For the commands, the <source> argument must always refer to the entire DB, and only the <overlaps> arguments can involve a block number. If <overlaps> involves a block number, e.g.
Ecoli.2.las, then the .las file is expected to contain all the overlaps where the A-read is in block 2 of the underlying database. The HPC.daligner scripts in the
DALIGNER module produce such .las files as their final result. Parameters are propagated
down the pipeline to subsequent phases via the annotation tracks so one need not specify
the same parameter over and over again, i.e, the parameters/flags -H, -c, -g, and -b. In this version of the pipeline the parameters -c, -g, and -b are estimated directly from the data, but a user may over-ride the system inferred values.
All programs add suffixes (e.g. .db, .las) as needed. For the commands that take multiple .las block files as arguments, e.g. DAScover, DASqv, …, one can place a @-sign in the name, which is then interpreted as the sequence of files obtained by replacing the @-sign by 1, 2, 3, … in sequence until a number is reached for which no file matches. One can also place a @-sign followed by an integer, say, i, in which case the sequence starts at i. Lastly, one can also place @i-j where i and j are integers, in which case the sequence is from i to j, inclusive.
DAScover [-v] [-H<int>] <subject:db> <overlaps:las> ...
This command takes as input a database <source> and a sequence of sorted local alignments blocks, <overlaps>, produced by an overlap/daligner run for said database.
Note carefully that <source> must always refer to the entire DB, only the <overlaps> can
involve block numbers.
Using the local alignment-pile for each A-read, DAScover produces a histogram of the depth of coverage of each trace point tile that is not within one of the intervals of the optionally specified tracks. It places this histogram in a .covr track for the bock and these block tracks are merged later with Catrack. If the -v option is set, the histogram for each block is displayed and an estimate of the coverage of the underlying target genome is output.
The -H option is for HGAP-based assembly (see the -H option of daligner) wherein only reads longer than the -H parameter are considered for overlap, scrubbing, and assembly. With this option set, DASqv and all subsequent commands in the scrubbing pipeline, only perform their functions on reads of length -H or more. All other reads are used in the overlap piles for H-reads to help assess and scrub the H-reads, but are themselves not scrubbed.
If the overlap file contains a block number then the track files also contain a block number, e.g. “
DAScover DB OVL.2” will result in the track files
DB.2.cover.[anno,data]. Furthermore, if DAScover is run on .las blocks, then once it has been run on all the blocks of the DB, the block tracks must be concatenated into a single track for the entire database with Catrack in preparation for the next phase of scrubbing by DASqv.
DASqv [-v] [-H<int>] [-c<int>] <subject:db> <overlaps:las> ...
This command takes as input a database <source> and a sequence of sorted local alignments blocks, <overlaps>, produced by an overlap/daligner run for said database. A .covr track obtained by running DASqv and Catrack must be present for the entire data base. Note carefully that <source> must always refer to the entire DB, only <overlaps> can involve block numbers.
Using the local alignment-pile for each A-read, DASqv produces a QV value for each complete trace point tile of TRACE_SPACING bases (e.g. 100bp, the -s parameter to daligner). The quality value of of each trace tile is the average of the best 25-50% of the estimated coverage alignment matches, where the estimated coverage is computed from the histogram of the .covr track. If one supplies the -c parameter, than this value explicitly overrides the estimated coverage produced by default. All quality values over 50 are clipped to 50.
The -v option prints out a histogram of the tile alignment differences, and another of the quality values produced. This later histogram is useful in assessing, for a given data set, what constitutes the threshold -g and -b, to be used by down stream commands, for what is definitely a good segment and what is definitely a bad segment. By default, the system will choose for -g the QV value for which 80% or more of the QVs are better than this value, and for -b, the QV value for which 7% or more of the QVs are worse than this value.
The quality values are written to a .qual track, that can be viewed by calling DBdump with the -i option set (“i” for “intrinsic QV”). Like DAScover and all other scrubber modules, block tracks are produced in response to block .las files and these must be concatenated with Catrack into a single .qual track for the entire DB in preparation for the next phase of scrubbing by DAStrim.
DAStrim [-v] [-g<int>] [-b<int>] <subject:db> <overlaps:las> ...
A DB-wide .qual track produced by DASqv and Catrack are required as input to this command. This command further takes as input a database <source> and a sequence of sorted local alignments blocks, <overlaps>, produced by an overlap/daligner run for said database. Note carefully that <source> must always refer to the entire DB, only <overlaps> can involve block numbers.
Using the local alignment-pile for each A-read and the QV’s for all the reads in the pile, DAStrim (1) finds and breaks all chimeric reads, (2) finds all missed adaptamers and retains only the longest subread between missed adaptaers, and (3) identifies all low-quality regions that should be improved/replaced by better sequence. It makes these inherently heuristic decisions conservatively so that what remains is very highly likely to be free of chimers, adaptamers, and undetected low-quality sequence segments. Some of these artifacts may still get through, but at very low odds, less than 1 in 10,000 in our experience. The decision process is guided by the parameters -g and -b which indicate the thresholds for considering intrinsic QV values good, bad, or unknown. By default these parameters are automatically set to be the 80’th and 93’rd percentiles of the qv-histograms hidden in the .qual track. They may however be explicitly set at the command line to over-rule this default choice.
The -v option prints out a report of how many chimer and adaptamer breaks were detected, how much sequence was trimmed, how many low-quality segments were spanned by alignments, and how many were rescued by many pairs of local alignments spanning the gap indicued by the low-quality region, and so on.
The retained high-quality intervals for each read are written to a .trim track, in left-to-right order with an indicator of whether the gap between two such intervals is spanned by local alignments or by span-consistent pairs of local alignments. Like DAScover and all other scrubber modules, block tracks are produced in response to block .las files and these must be concatenated with Catrack into a single .trim track for the entire DB in preparation for the next phase of scrubbing by DASpatch.
DASpatch [-v] <subject:db> <overlaps:las> ...
This command takes as input a database <source> and a sequence of sorted local alignments blocks, <overlaps>, produced by an overlap/daligner run for said database. A .qual track and a .trim track obtained by running DASqv and DAStrim must be present for the entire data base. Note carefully that <source> must always refer to the entire DB, only <overlaps> can involve block numbers.
Using the local alignment-pile for each A-read, the QV’s for all the reads in the pile, and the hiqh-quality segments annotated by DAStrim, DASpatch selects a high-quality B-read segment with which to patch every intervening low-quality segment of an A-read. Given that these gaps are annotated before each read is trimmed by DAStrim, it may be the case in this second examination, that the gap is no longer spanned by the now trimmed B-reads in which case the span/patch can fail. This is very rare but does occur and is the number of such events is reported by DASpatch when the -v option is set.
The B-read segments for each patch (or a special “failure patch”) are written to a .patch track, in left-to-right order. Like DAScover and all other scrubber modules, block tracks are produced in response to block .las files and these must be concatenated with Catrack into a single .patch track for the entire DB in preparation for the next phase of scrubbing by DASedit.
DASedit [-v] [-x<int>] <source:db> <target:db>
This command takes as input a database <source> for which a .trim, and .patch tracks have produced by DAStrim and DASpatch in sequence (and perforce DAScover and DASqv initially). Using the information in the two tracks, DASedit produces a new database <target> whose reads are the patched, high-quality sub-reads. That is, every low quality segment is patched with the relevant sequence of another read, and some reads give rise to two or more reads if deemed chimers, or no reads if the entire read was deemed junk. This command can take considerable time as the access pattern to read sequences (for the patching) is not sequential or localized, implying poor cache performance.
The new database does not have a .qvs or .arr component, that is, it is a sequence or S-database (see the original Dazzler DB post). Very importantly, the new database has exactly the same block divisions as the original. That is, all patched subreads in a block of the new database have been derived from reads of the same block in the original database, and only from those reads. The new database does have a .map track that for each read encodes the original read in the source DB that was patched and the segments of that read that were high-quality (i.e. not patched). The program DASmap below can be used to output this information in either an easy-to-read or an easy-to-parse format.
DASmap [-p] <path:db> [ <reads:FILE> |<reads:range> ... ]
This command takes as input a database of patched reads <source> produced by DASedit. and for the specified reads outputs a line for each showing the source read index and length in the originating DB, as well as annotating which segments were original and which were patched. The convention on interpreting the read arguments is as for DBshow and DBdump. As an example, with the -p option (pretty print) set one might see:
55 -> 57(2946) [400,2946]
56 -> 58(11256) [700,1900]
57 -> 58(11256) [6600,9900] 83 [10000,11256]
58 -> 59(12282) [400,4100] 88 [4200,9400] 97 [9500,12282]
The first line indicates that read 55 in the patched database was derived from read 57 in the original database and is the segment from [400,2946] of that read. Reads 56 and 57 were both derived from read 58 in the original DB, and read 57 consists of segments [6600,9900] and [10000,11256] of read 58 with a patch between them of 83bp (but the source of the patch data is not given). The read length of each original read is given for convenience. With the -p option off, the output consists of space separated integers encoding the same information where the 4th field is always the number of integers in the segment description (always 3n+2 for some n):
55 57 2946 2 400 2946
56 58 11256 2 700 1900
57 58 11256 5 6600 9900 83 10000 11256
58 59 12282 8 400 4100 88 4200 9400 97 9500 12282
DASrealign [-v] [-l<int:(800)>] <block1> <block2> <source:las> <target:las>
This command takes as input two blocks of a patched database <source> created by DASedit and the original .las file <overlap> for the block pair. That is the .las file that was produced for the blocks when daligner was run on the original database blocks. DASrealign then produces the set of alignments (inferrable from those in the original) between the new patched reads, placing them in the file <target>. These new .las files can then be merged with LAmerge to form block .las files for the new database.
The idea of this program is to avoid having to run daligner again on the patched reads, but rather to simply refine the alignments already computed with respect to the new read set. It has the draw back that there is some small chance that there are previously undetected overlaps between reads now that they are patched, and the patched trace point encoding, while stillable to deliver alignments are no longer usable for quality estimation as the tracepoint spacing in the A-read becomes irregular. This is contrasted with the speedup of the new process which is roughly 40X faster than the original daligner run and the collection of overlaps in the output can be feed directly into a string graph construction process.
REPcover <subject:db> ...
This command takes as input a sequence of databases or blocks <source> and for each outputs a histogram of the coverage of the unmasked portions of the reads in the source along with a recommendation of the -c value with which to run DASqv. The .covr track produced by DAScover must be present for all sources referred to. The command is a quick way get the -v output of DAScover at any time after producing the coverage histograms and to get the histogram and coverage estimate for the entire data base (as opposed to a block of the database).
REPqv <subject:db/block> ...
This command takes as input a sequence of databases or blocks <source> and for each outputs a histogram of the intrinsic quality values of the reads in the source along with a recommendation of the -g and -b values with which to run DAStrim. The .qual track produced by DASqv must be present for all sources referred to. The command is a quick way to get the -v output of DASqv at any time after producing the intrinsic quality values and to get the histograms for the entire data base (as opposed to a block of the database).
REPtrim <subject:db/block> ...
This command takes as input a sequence of databases or blocks <source> and for each outputs the scrubbing statistics for the source, i.e. the same report produced by DAStrim with the -v option set. The .trim track produced by DAStrim must be present for all sources referred to. The command is a quick way to get the -v output of DAStrim at any time after the fact and to get the statistics for the entire data base (as opposed to a block of the database).