How It Works

Workflow of SMAP haplotype-window

procedureoverviewrequired input

Step 1

SMAP haplotype-window extracts haplotypes from reads aligned to a predefined set of loci, here called Windows, in a reference sequence. Each Window is enclosed by a pair of Border regions.

Border regions can be defined at primer binding sites, either with (Window 1) or without (Window 2) an off-set. Borders can be of variable length, defined by the user (typically 5-10 bp). Pairs of Borders can also be defined so that they enclose Sliding frames, for instance to process Shotgun data.

Step 2

Read mapping is used to assign reads to their corresponding locus on the reference genome.

Consider a sequenced fragment derived from a given genomic locus with a large deletion, or highly polymorphic region with multiple flanking SNPs, in the middle of the fragment.

Two flanking primers can bind the genome sequence of the sample and can amplify the fragment. Also, the two regions flanking the central polymorphism in the same read contain (near-)exact sequence similarity to the reference sequence of the genomic locus.

Mapping reads with BWA-MEM, defines which genomic locus is the origin of the sequenced fragment (the maximal exact match that seeds the alignment), and extends the alignment outwards untill a maximum number of read-reference mismatches is reached.

If read-reference alignments are truncated before the end of the read, BWA-MEM removes the unmapped region of the sequence read in the resulting BAM file (called soft-clipping).

Consequently, the sequence of a given read in the FASTQ file (before read mapping) may have a different length compared to the corresponding read in the BAM file (after mapping).

Also, the polymorphisms that caused the truncation of the read alignment are no longer present in the BAM file (not as alignment, not as FASTQ sequence data), and can not be used to detect polymorphisms by direct read-reference alignment comparison.

Step 3

For each Window, SMAP haplotype-window will define the Window region in the reference genome by pairing Border regions defined in a GFF file.

For each BAM file and for each Window, SMAP haplotype-window will identify the IDs of reads that overlap with at least one nucleotide for a given Window, retrieve their original complete read sequence from the corresponding sample’s FASTQ file and create a separate FASTQ file for each sample-Window combination.

These steps make sure that reads that are soft-clipped during read alignment by BWA-MEM but that initially do contain the Border sequences at their respective ends, can still be evaluated in their entirety. Soft-clipping results in partial read alignment and removal of the unmapped part of the sequence read from the BAM file.

SMAP haplotype-window then retrieves the respective sequences for the upstream Border and downstream Border regions using the GFF coordinates and the reference genome FASTA sequence for each Window.

Step 4

All separate FASTQ files (one for each sample-Window combination) are then passed to Cutadapt using the Window-specific pair of Border sequences for pattern trimming.

Because the Window is defined as the region inbetween the Borders (i.e. read regions retained after removal of the Borders), the entire read sequence spanning the Window is considered as a unique haplotype.

Step 5

These haplotypes are then counted per Window per sample, optionally filtered for (min/max) total read count per Window per sample.

All individual sample specific haplotype count tables are integrated into a large haplotype count matrix.

This procedure detects unique haplotypes in Windows enclosed by two known Border sequences consisting of any (a priori unknown) combination of InDels and/or SNPs, without using the BAM alignment itself for the detection of InDels and/or SNPs. The BWA-MEM alignment is merely used for efficiently sorting reads across the reference genome and grouping by locus.

Haplotype counts are converted into relative frequencies, which can then be filtered to remove low-frequency noise.

The final step of SMAP haplotype-window is only applicable on individuals and converts haplotype frequencies into discrete calls.

Using customizable frequency intervals, haplotype frequencies can either be transformed into dominant calls (0/1) or dosage calls (0/1/2/..).

CRISPR/Cas extension of SMAP haplotype-window

A specific extension of the SMAP haplotype-window workflow for CRISPR data can be invoked using the optional command --guides.

If CRISPR/Cas genome editing was performed by stable transformation with a CRISPR/gRNA delivery vector, then the presence of the gRNA cassette in the delivery vector may be detected in the transformed genome. Primers can be designed on the vector sequence to amplify the gRNA sequence in the gRNA expression cassette, and Border regions can be positioned directly flanking the 20 bp gRNA sequence. The haplotype of that ‘locus’ that is then detected is effectively a copy of the gRNA sequence incorporated into the transformed genome. These primers can be included in the HiPlex primer set used to screen for the genomic target loci. SMAP haplotype-window can assign gRNA vector-derived reads to the respective target loci, if the user provides a FASTA file with the target loci names as identifiers and the 20 bp gRNA as sequence. In this way, genome-edited haplotypes at genomic target loci can be detected in parallel to the gRNAs that cause them, for any number of loci and any number of samples.

Example of gRNA sequences FASTA:

>AT1G07650_1_gRNA_001
TGAAGTCGCAGAACTTAACG
>AT1G07650_1_gRNA_002
CTGAAGTCGCAGAACTTAAC

Example of output file with diverse genome-edited haplotypes at genomic target loci and corresponding gRNA. By sorting on the fourth column (Target) in any output .tsv file, it is possible to arrange all the target loci with their corresponding gRNAs. Note that the standard output of SMAP haplotype-window can be further annotated by SMAP effect-prediction, for instance as the length difference with the reference, or even with the effect of the mutation on the predicted protein, in case candidate genes are used as reference sequence.

Unsorted output fileSorted output file

Reference	Locus	Haplotypes	Target	Sample1	Sample2	Sample3	Sample4	Sample5	Sample6	Sample7
AT1G07650	AT1G07650_1	GAGCTCTGAAGTCGCAGAACTTAACAGGCATTGTCCCTCCAGAATTCTCTAAGCTTCGTCACCTTAAAGTTTTG	AT1G07650_1	100	100	100	100		100	100
AT1G07650	AT1G07650_2	AAGTGATAATAATTTCACTGGACCAATACCAGATTTCATCAGCAATTGGACAAGAATACTGAAACTGTACGATCTATTCTTT	AT1G07650_2	100	100	100	100	100	100	100
AT1G07650	AT1G07650_3	CTTGTGAAGCTATATGGATGTTGTGTTGAAGGAAACCAATTGATATTGGTGTATGAGTACTTGGAGAACAACTGTCTA	AT1G07650_3	100	100	100	100	100	100	100
AT2G02780	AT2G02780_1	CTTAGCTCTAATTTCATCTCTGGGAAGATTCCAGAAGAGATTGTGTCTTTGAAGAATCTGAAAAGCCTTGTTTTA	AT2G02780_1	100	100	100	100	100	100	100
AT2G02780	AT2G02780_2	CATTGAAGAACAACTCCTTTAGATCCAAGATTCCAGAACAGATCAAGAAGCTGAATAACCTTCAAAGTTTAGACCTTTCT	AT2G02780_2	100	100	100	100	100	100	100
AT2G02780	AT2G02780_3	TTTCTGAGTGTAGGACGTGTACCACAGACAATGAGATCTGCAGTAGATTGGTTTGCCACCGTACCGCGTTTTCTCCTTGGAG	AT2G02780_3	6.22	6.12
AT2G02780	AT2G02780_3	TTTCTGAGTGTAGGACGTGTACCACAGACAATGAGATCTGCAGTGATTGGTTTGCCACCGTACCGCGTTTTCTCCTTGGAG	AT2G02780_3	73.88	54.69	100	100	100	100	100
AT2G02780	AT2G02780_3	TTTCTGAGTGTAGGACGTGTACCACAGACAATGAGATCTGCAGTTGATTGGTTTGCCACCGTACCGCGTTTTCTCCTTGGAG	AT2G02780_3	19.92	39.22
AT2G16250	AT2G16250_1	AGCGTTACCGGGGACTATACCTGAGTGGTTTGGTGTTAGTTTGTTGGCATTGGAAGTATTGGATCTTAGTTCGTGT	AT2G16250_1	100	100	100	100	100	100	100
AT3G03770	AT3G03770_1	ACGCTTATACTCGACGAGAATATGTTTTCTGGTGAGTTACCCGGATTGGATTGATTCTTTGCCGAGTTTAGCTGTGTTGAGCTTGAG	AT3G03770_1		2.22
AT3G03770	AT3G03770_1	ACGCTTATACTCGACGAGAATATGTTTTCTGGTGAGTTACCGAGATTGGATTGATTCTTTGCCGAGTTTAGCTGTGTTGAGCTTGAG	AT3G03770_1	9.46	15.87
AT3G03770	AT3G03770_1	ACGCTTATACTCGACGAGAATATGTTTTCTGGTGAGTTACCGATTGGATTGATTCTTTGCCGAGTTTAGCTGTGTTGAGCTTGAG	AT3G03770_1	10.81	23.62
AT3G03770	AT3G03770_1	ACGCTTATACTCGACGAGAATATGTTTTCTGGTGAGTTACCGGATTGGATTGATTCTTTGCCGAGTTTAGCTGTGTTGAGCTTGAG	AT3G03770_1	72.06	39.84	100	100	100	100	100
AT3G03770	AT3G03770_1	ACGCTTATACTCGACGAGAATATGTTTTCTGGTGAGTTACCGTGATTGGATTGATTCTTTGCCGAGTTTAGCTGTGTTGAGCTTGAG	AT3G03770_1	7.66	18.44
AT3G03770	AT3G03770_2	TGGATCTTTCCTACAATACATTCGTTGGTCCATTTCCTACTTCCTTGATGTCTCTTCCCGCGATAACTTACTTGAACA	AT3G03770_2	100	100	100	100	100	100	100
AT3G03770	AT3G03770_3	TTTTCATTGTTCTTTTCAGATTTATAGAGGGAGATTGAAAGATGGATCCTTTGATGGCAATTAGGTGTCTGAA	AT3G03770_3	7.94	14.58
AT3G03770	AT3G03770_3	TTTTCATTGTTCTTTTCAGATTTATAGAGGGAGATTGAAAGATGGATCCTTTGTGGCAATTAGGTGTCTGAA	AT3G03770_3	42.09	9.28	100	100	100	100	100
AT3G03770	AT3G03770_3	TTTTCATTGTTCTTTTCAGATTTATAGAGGGAGATTGAAAGATGGATCCTTTGTTGGCAATTAGGTGTCTGAA	AT3G03770_3	50	76.19
AT3G08680	AT3G08680_1	TATCCCTCCCGTCCTTTCTCATCGCCTCGTTAATCTTGATCTCTCCGCCAACTCGCTCTCGGGAAACATTCCCACGAGTCTACAA	AT3G08680_1	100	100	100	100	100	100	100
AT3G08680	AT3G08680_2	TCTCCTTCACCGACAACACCAACAGAAGGTCCTGGTACAACCAATATAGGTCGTGGTACCGCCAAAAAAGTTCTCTCCACTGGTGCTATTGT	AT3G08680_2	100	100	100	100	100	100	100
AT3G08680	AT3G08680_3	AGAAGTAGCAGCTGGAAAGAGGGAGTTCGAGCAGCAGATGGAAGCCGTGGGAAGGATCAGTCCACACGTGAAT	AT3G08680_3			1.14				1.3
AT3G08680	AT3G08680_3	AGAAGTAGCAGCTGGGAAACGGGAGTTCGAGCAGCAGATGGAAGCCGTGGGAAGGATCAGTCCACACGTGAAT	AT3G08680_3	100	100	98.88	100	100	100	98.75
AT3G13065	AT3G13065_1	TCTCGTTTTCTGTTTCTGTAGAAAAGTATCTGGACGTGGACTCAGTGGATCTTTAGGTTACCAGCTTGGAAA	AT3G13065_1	100	100	100	100	100	100	100
AT3G24660	AT3G24660_1	GTTTTGCCTCCATCGATTTGGAACCTCTGTGATAAGCTTGTCTCTTTCAAGATTCATGGTAATAACTTGTCTGGGGTTTTGCC	AT3G24660_1	100	100	100	100	100	100	100
AT3G24660	AT3G24660_2	GATTTTGGTGAGTCAAAGTTTGGAGCAGAATCTTTCGAAGGGAACAGTCCTAGCCTTTGTGGTTTGCCTTTGAAGC	AT3G24660_2	100	100	100	100	36.25	100	100
AT3G24660	AT3G24660_2	GATTTTGGTGAGTCAAAGTTTGGAGCAGAATCTTTCGAAGGGAACAGTCCTAGGACTGCATGGTATCTTTGTGGTTTGCCTTTGAAGC	AT3G24660_2					63.81
AT3G51740	AT3G51740_3	GATTCGTCATCAGAATCTTCTTGCACTAAGAGCTTACTACTTAGGACCTAAAGGAGAGAAGCTTCTCGTCTTTGATTACATGCCTAAAAGC	AT3G51740_3	5.02	3.42	4.57	4.63	6.12	3.97	6.25
AT3G51740	AT3G51740_3	GATTCGTCATCAGAATCTTCTTGCACTAAGAGCTTACTACTTAGGACCTAAAGGAGAGAAGCTTCTCGTCTTTGATTACATGTCTAAAGGA	AT3G51740_3	95.06	96.56	95.44	95.38	93.81	88.06	88.38
AT3G51740	AT3G51740_3	GATTCGTCATCAGAATCTTCTTGCACTAAGAGCTTACTACTTAGGACCTAAAGGGAGAGAAGCTTCTCGTCTTTGATTACATGTCTAAAGGA	AT3G51740_3						7.94	5.36
AT5G20690	AT5G20690_2	TCCTCGGTACCAGAAACCTCGAAACAAGGCCGCAATAAACGCAATCATGGTGTCGATATCGCTTCTTCTCCTGTTCTTTATT	AT5G20690_2			25.41	25.73
AT5G20690	AT5G20690_2	TCCTCGGTACCAGAAACCTCGAACAAGGCCGCAATAAACGCAATCATGGTGTCGATATCGCTTCTTCTCCTGTTCTTTATT	AT5G20690_2	100	100	74.62	74.25	100	100	100
CRISPR_Vector	gRNA	ACTCATACACCAATATCAAT	AT1G07650_3_gRNA_006			22.55	24.22
CRISPR_Vector	gRNA	ACAATGAGATCTGCAGTGAT	AT2G02780_3_gRNA_077	45.09	46.81
CRISPR_Vector	gRNA	TTTGTTGGCATTGGAAGTAT	AT2G16250_1_gRNA_080					61.53
CRISPR_Vector	gRNA	TTTCTGGTGAGTTACCGGAT	AT3G03770_1_gRNA_109	54.88	53.19
CRISPR_Vector	gRNA	AAAGGCAAACCACAAAGGCT	AT3G24660_2_gRNA_142					38.5
CRISPR_Vector	gRNA	ACGAGAAGCTTCTCTCCTTT	AT3G51740_3_gRNA_150						29.88	30.45
CRISPR_Vector	gRNA	GGTACCAGAAACCTCGAACA	AT5G20690_2_gRNA_213			23	21.62
CRISPR_Vector	gRNA	TAGAAGACGTTGACCATGCT	gRNA_1						40.25	39.34