FAQs

Your questions, answered: find clear and detailed answers to your most common questions

Logistics/Storage

No. Please email technical support at [email protected] and explain the situation. Our experts will help you.
The document is included inside both packages. If you need an additional copy please send an email to [email protected] and specify the purchase order number in your email.
You can request this information in the OR-Ticket assigned to your order.
Our laboratory organises the shipment (pickup) of both boxes the same day but occasionally the two boxes may continue the journey separately.

The second box should arrive soon.

Our logistics team is constantly tracking all your shipments to make sure you will be delivered as soon as possible.
The logistics team continually tracks all shipments to ensure rapid delivery to you. The laboratory organizes the shipment (pick up) of both boxes on the same day however, depending on the transporter, the boxes might continue the journey separately.
Please contact our support by sending an email to this address: [email protected] You can explain the situation and our experts will take care of this request.
Please email technical support at [email protected] and explain the situation. Our experts will take care of your request.
Please email technical support at [email protected] and explain the situation. Our experts will take care of your request.
You can request this information in the OR-Ticket assigned to your order.
The BDS number can be found at two different locations:

On the side sticker of Box 1 of the SOPHiA GENETICS Bundle Solution (-15 °C to -25 °C storage temperature)
In the annex of the delivery note
For detailed instructions please refer to the Operation Manual Section 2.3.

Figure 1: Location of BDS numbers on SOPHiA GENETICS Bundle Solution kit box 1

Note: each BDS number is related to one lot number and Bundle Solution kit box. If different members of your team conduct library preparation and sample upload, please make sure that they are all informed about this implementation.
You can request this information in the OR-Ticket assigned to your order.

NGS Lab

Typically, we obtain between 800ng and 1.2ug of DNA per reaction for the LPI kit, when starting from 200ng of good quality genomic DNA, and between 1 and 1.8 ug of DNA per reaction for LPII kit when starting with 50 ng of good quality genomic DNA. This corresponds to a concentration between 40 and 60 ng/ul for LPI and 45 and 110 ng/ul for LPII.
If the stringent wash is performed at a temperature above 65°C, or for more than 5 minutes, and/or not gently enough, preferential loss of AT-rich regions over the GC rich regions my occur, leading to a decrease in coverage uniformity.
On-target rates are expected to be around 70 – 80 %. Low on-target rates are caused by problems in the hybridization and capture step and are also reflected in a high number of low coverage regions. On-target rates below 10 % have been observed when one of the following experimental errors occurred:

Probes were not added to the hybridization reaction, resulting in libraries without target enrichment. We suggest checking the remaining volumes in the probe tube, to make sure that the correct volume has been removed.

The hybridization reaction was not placed at 95°C before adding the probes and starting the hybridization. In this scenario, the double stranded DNA is not denatured, hence, no binding between probes and DNA can occur.

The hybridization reaction cooled down after DNA denaturation. If the reaction is placed at room temperature for too long, the DNA strands re-anneal, and the probes cannot access the DNA. This can also occur when the same thermal cycler is used for both denaturation and hybridization. In this case, the thermal cycler might take too long to cool down from 95°C to 65°C, and during this time the DNA stands anneal again. We recommend using to different blocks for denaturation and hybridization.

Cot DNA was not added to the hybridization reaction. Cot I DNA is used to block non-specific hybridization of repetitive sequences. We suggest checking the remaining volume in the tube, to make sure that the correct volume was removed.
Universal blockers were not added to the hybridization reaction. The universal blocking oligos prevent non-specific hybridization between adapter sequences, thereby enhancing specificity of the capture. We suggest checking the remaining volumes in the probe tube, to make sure that the correct volume was removed.
Generally, for Illumina sequencers, you can use different sets of indexes in the same run if they have the same length. Indeed, you may be able to multiplex, in the same run, libraries obtained using our Bundle Solutions and libraries performed with other kits as long as they use:

– non redundant index sequence of 8bp
– standard sequencing primers from Illumina

Moreover, when sequencing libraries are generated with Bundle Solutions from SOPHiA GENETICS, the complexity of sequencing parameters is reduced, thus enabling multiplexing with other kits in all Illumina instruments.

On the MiniSeqTM, however, simultaneous analysis of both kits is not possible. In this case, you can still sequence the samples together, but you need to perform the analysis twice. You can start by analyzing the samples from the other commercial kit and then re-import the run into a new analysis once the first has completed.
We recommend any fluorometric-based quantification method such as Qubit HS.
Heterogeneity of coverage at that level observed for all samples may be caused by suboptimal capture washes. A stringent wash not performed carefully enough (2 times for 5 minutes at 65 °C and not above this temperature) can result in preferential loss of the AT-rich regions over the GC rich regions, leading to a high coverage heterogeneity. The nature and the quality of the DNA samples can also lead to high coverage heterogeneity, as is frequently observed with FFPE samples.

Moreover, evaporation may occur during the O.N. hybridization. Ensure that the 0.2 ml tubes are tightly closed to avoid leakage over time.
1) Imprecise quantification or pipetting during pooling of libraries

2) Individual library yield is suboptimal resulting in suboptimal capture (Starting material is too low, Degraded DNA)

3) Individual Library are not in the optimal size range. (Degraded DNA, Suboptimal fragmentation or dual size selection).
An incomplete mixing of the pre-mixes is one of the possible explanations. Make sure the reagents are completely thawed and homogeneous prior to use.

Some other reasons include:

Wrong storage of the kit(s),
Insufficient amount of starting input,
Ethanol used at the wrong step or at the wrong concentration,
Equipment issue (thermocycler, multi-channel pipette, …),
AMPure beads not equilibrated at room temperature,
Discarding the supernatant during the dual size, elution with water or IDTE at the wrong step of the protocol.
If the yield is too low to proceed to the pooling step for a given sample, we recommend re-preparing the libraries.

Pooling various amounts of libraries together will create a read imbalance between samples and potentially cause the CNV detection to fail for some samples.
Usually with most of our panels the amount of DNA we quantify after the capture is between 10 and 30 ng/µl. Low yields may be related to several problems:

- Incomplete denaturation step
- A decrease of temperature after denaturation and prior to the addition of the probes
- Omission of probe addition
- Loss of material during the wash steps (?)
- Problems with post-capture amplification
- Loss of material during cleanup
On-target rates are expected to be around 70 – 80 %. Low on-target rates are caused by problems in the hybridization and capture step and are also reflected in a high number of low coverage regions. On-target rates below 10 % have been observed when one of the following experimental errors occurred:

- Probes were not added to the hybridization reaction, resulting in libraries without target enrichment. We suggest checking the remaining volumes in the probe tube, to make sure that the correct volume has been removed.
- The hybridization reaction was not placed at 95°C before adding the probes and starting the hybridization. In this scenario, the double stranded DNA is not denatured, hence, no binding between probes and DNA can occur.
- The hybridization reaction cooled down after DNA denaturation. If the reaction is placed at room temperature for too long, the DNA strands re-anneal, and the probes cannot access the DNA. This can also occur when the same thermal cycler is used for both denaturation and hybridization. In this case, the thermal cycler might take too long to cool down from 95°C to 65°C, and during this time the DNA stands anneal again. We recommend using to different blocks for denaturation and hybridization.
- Cot DNA was not added to the hybridization reaction. Cot I DNA is used to block non-specific hybridization of repetitive sequences. We suggest checking the remaining volume in the tube, to make sure that the correct volume was removed.
- Universal blockers were not added to the hybridization reaction. The universal blocking oligos prevent non-specific hybridization between adapter sequences, thereby enhancing specificity of the capture. We suggest checking the remaining volumes in the probe tube, to make sure that the correct volume was removed.

SOPHiA DDM™ – General/How to

The Variant Database Browser (VDB) can be filtered according to various pre-set parameters such as:

– Chromosome number
– Genomic position
– Gene name
– Specific position of cDNA or protein annotations

Once information has been added to the filters, the VDB will display the first 500 variants matching this filter within your account. For detailed instructions please refer to the Operation Manual.
This filter is designed to reduce the number of false positive (FP) variant calls and avoid false negatives (FN) in homopolymer regions on IonTorrent platforms. It works by distinguishing systematic errors from the real biological variant signal. The signal is compared across samples, and if a certain pattern is found in every sample it is regarded as a likely systematic error and not real.

This variant caller handles only variants in homopolymers (i.e., variants that change the length of the homopolymer) or small INDELs like single nucleotide insertions or deletions, as such variants are known to be most affected by errors of the IonTorrent platform. This filter does not affect SNPs or other types of INDELs.

IMPORTANT NOTE: The module requires a minimum of 4 samples (minimum 8 recommended). Fewer than 4 samples will not benefit from the multi-sample variant caller, leading to potential differences in the reported variants.

TIP: only samples processed together should be analysed together, as error profiles can be different between runs.
It is only possible to visualize what a bioinformatic pipeline reports, i.e. variants below the pipeline-defined cut-off cannot be visualized in the platform. The custom filter builder can be used to display variants with low variant fractions. For detailed instructions please refer to the Operation Manual.

Alternatively, follow these simplified instructions:
1) New filters use only retained variants by default. Low confidence variants can be added by dragging the built-in rule “Low confidence” onto the left bar with the initial variants count. This ensures your new filter contains both retained and the low-confidence variants.

2) Create a filter for the low-confidence variants based on the filter value. Select “Filter” from the drop down box on the top right corner and drag it onto the “”low confidence variants”” box. By selecting this feature the filter value should be “low_variant_fraction”, and as a result, only low-confidence variants that were previously rejected for low variant fraction will now be kept.

3) This filter may be further adapted to include other parameters such as applicability to only variants in certain genes, of certain coding consequences, etc..

SOPHiA DDM™ – How to interprete results/Bioinformatics explanations

There are several reasons why the SOPHiA DDM™ Platform may classify a variant as “low-confidence”.

1. The variant may occur in a low complexity region (e.g. homopolymers or heteropolymers). Such regions adversely affect amplification (polymerase slippage) and hinder reliable variant calling. Variants in other challenging regions from the reference genome (e.g., high/low GC, long tandem repeats, regions with high homology to other regions in the genome, …) can be assigned a “problematic region” tag, as can variants from regions leading to noises specific to the sequencing platform or the NGS chemistry.

2. The variant may have a variant fraction lower than expected (germline) or lower than what can be confidently called with a statistical test (somatic). Variants with low variant fractions are filtered as “low_variant_fraction”.

3. A variant with low coverage is filtered with a “low_coverage” tag. Many germline solutions give warnings for regions covered with less than 50x and any variant detected in a region with less than 30x will be classified as low-confidence. The exact thresholds may vary between solutions.

4. Variants outside the target region of the solution are filtered as ‘off-target’. For capture-based solutions, many off-target variants may be observed in regions flanking the target region that still have sufficient coverage for reliable variant calling.

5. INDELs in long homopolymers are filtered as ‘homopolymer_region’. Long homopolymers impede reliable variant calling due to experimental artefacts like polymerase slippage leading to elevated sequencing errors. A high error rate in homopolymers is often observed on most sequencing platforms. Therefore, any INDEL identified in homopolymers greater than a certain length (exact length cut-offs are solution-specific) are filtered as “homopolymer_region”.
In the SOPHiA DDM™ Platform, the Coverage Calculator shows the worst-case minimum coverage among all the transcripts. If 0 coverage is observed for some transcripts in a gene, the entire gene is listed with 0 coverage. This can be modified by clicking on the “arrow” next to the gene name, to view all transcripts with well covered exons, as well as transcripts with 0-covered exons within the same gene. For detailed instructions please refer to the Operation Manual Section 4.8.
Variants displayed within the SOPHiA DDM™ Platform are relative to the reference genome (hg19).

There are some cases where the reference genome contains a minor allele. In these cases, RefSeq sequences overlapping this region may differ from the reference genome and show the major allele instead. Comparing the sequencing data with the RefSeq sequence, no variant will be identified. However, since the SOPHiA DDM™ Platform uses the reference genome, it will systematically detect a difference and report it.

TIP : The population frequency of the variant is provided within the SOPHiA DDM™ Platform, making it easy to spot that the variant has a population frequency of almost 100% (and therefore is the major allele) and not clinically relevant.
Variants outside the target region defined by the solution are filtered as “off-target”.

– For capture-based enrichment, many off-target variants may be observed in regions flanking the target region that still have sufficient coverage for variant calling.

– For amplicon-based enrichment, if the variant is located in the region covered by a primer and no other amplicons overlap this region, they are classified as “off target”.
The Variant Filter Builder is a cross functional tool that allows you to create and apply custom filters to variants based on different operators / variables and built-in rules, including the ability to set custom thresholds.

For example, when setting a custom threshold for the pathogenic community value:

– Add “level:count”. Here, the “level” refers to the pathogenicity flag, while the “count” refers to the number of times that this variant has been flagged as such.

If you already know the number of times that a specific pathogenicity flag has been used, you can use a definitive number with the setting of “MATCHES WITH”.

– Example: a variant that has been flagged as pathogenicity level 4, three times by the community. You will then add the following to the value field 4:3.

If you do not know the number of times that a specific pathogenicity flag has been used, you can use the wildcard symbol “*” that represents “anything”

– Example: a variant has been flagged as pathogenicity level 5, multiple times by the community. You will then add the following to the value field 5:*

If you would like to add multiple values, these can be added and separated by a comma.

– Example: a variant that has been flagged as pathogenicity level 4, three times by the community OR a variant that has been flagged as pathogenicity level 5, multiple times by the community 4:3, 5:*

For detailed instructions please refer to the Operation Manual Section 5.
Diseases can easily be added when a new interpretation project is created.

1) After selecting “Add interpretation”, a new window will appear.

2) In the pop-up window, a new link “select” is available.

3) Once clicked, the Disease Ontology tree opens to select a disease.

This disease tree can be searched or alternatively, you can start typing the respective disease name. The matching disorders are then immediately highlighted in the list.

TIPs:

– You can bookmark regularly selected diseases by clicking the “star” icon next to the disease.

– In cases where several diseases should be selected simultaneously, hold CTRL (MAC: cmd) while selecting several diseases.

For detailed instructions please refer to the Operation Manual Section 3.11.3.


Figure 1: Select a disease from any level of the ontology tree.

SEARCH FOR A DISEASE

The disease tree can be easily searched. Start typing the respective disease name. The list of terms containing the substring is instantly updated and resulting disorders are highlighted in the list.

BOOKMARK A DISEASE

Users can bookmark often selected diseases by clicking the “star” icon next to the disease. Alternatively, users can click Shift + B (Fig. 2). In both cases, the disease then appears and is saved in the list of bookmarks above the tree.


Figure 2: Bookmark a disease by clicking the “star” icon or pressing Shift + B.

MULTI-SELECT DISEASES

Several diseases can be selected and added to an interpretation project by clicking CTRL (Mac: cmd) while selecting them (Fig. 3).


Figure 3: Multi-select several diseases. Click to select a disease, hold CTRL pressed, select one or more other diseases. Click OK to confirm. All diseases are added to the interpretation project.
The SOPHiA DDM™ Platform normalizes the scores provided by the different tools to enable comparisons among the different sources. For all predictive scores, a value of 1 means likely pathogenic and 0 likely benign irrespective of the database. The scores are transformed following the rationale and procedure established by dbNSFP and discussed in Xiaoming Liu et al., 2011 (https://doi.org/10.1002/humu.21517). Briefly:

SIFT

“1 – SIFT” score (the original SIFT score is opposite, with 0 being predicted pathogenic and 1 benign)

PolyPhen2

No transformation (there are two different PolyPhen2 scores, HVAR and HDIV, based on different training sets)

MutationTaster

Combination of prediction and score (1 – confident, 0 not confident). The SOPHiA DDM™ Platform displays the score if the prediction is pathogenic or “1 – score” if the prediction is benign
The SOPHiA DDM™ Platform normalizes the scores provided by the different tools to enable comparisons among the different sources. For all predictive scores, a value of 1 means likely pathogenic and 0 likely benign irrespective of the database. The scores are transformed following the rationale and procedure established by dbNSFP and discussed in Xiaoming Liu et al., 2011 (https://doi.org/10.1002/humu.21517). Briefly:

SIFT

“1 – SIFT” score (the original SIFT score is opposite, with 0 being predicted pathogenic and 1 benign)

PolyPhen2

No transformation (there are two different PolyPhen2 scores, HVAR and HDIV, based on different training sets)

MutationTaster

Combination of prediction and score (1 – confident, 0 not confident). The SOPHiA DDM™ Platform displays the score if the prediction is pathogenic or “1 – score” if the prediction is benign
Variant detection occurs in multiple stages.

1) All the potential variants in the original read alignment are identified, and are subsequently evaluated to remove likely false positives (FP) while retaining high-confidence variants.

2) A closer investigation considers the reads supporting the variants, including information about other variants in the genomic neighborhood (e.g. by re-aligning the reads with larger gaps than allowed in the original alignment).

– In some cases, an INDEL can result in FP SNVs (e.g. if the reads do not span the entire INDEL). The SOPHiA DDM™ Platform identifies these FP artefacts and shifts the supporting reads accordingly (thus moving them from the FP to the real variant). In this situation, a variant can end up having 0% VF as the reads supporting it in the original alignment were shifted to another variant.

– FP artefact variants are kept in the SOPHiA DDM™ Platform to show you that they were found in the original BAM alignment file, though further analysis showed there is little support for them. Otherwise, you may think they were missed by the SOPHiA DDM™ Platform, especially if they were detected with a different software. So, in these cases the 0% variant fraction can be interpreted as an FP artefact.
The SOPHiA DDM™ Platform does not use paired-end information through Amplicon-based technology to detect structural rearrangements: genomic rearrangements cannot be detected. If a genomic rearrangement occurs in or adjacent to some amplicons, they will likely not amplify correctly. Thus, there would be no useable data.
The variant fraction of the INDEL is based on all the reads at the start position of the deletion (anchor position) after realignment, including reads that do not span the entire deletion. If other software only counts reads that span the deletion, the variant fraction could be different.
Non-Regression Reports are generated after major the SOPHiA DDM™ Platform updates that could affect variant detection. Such reports are sent at least one week before the Platform update. Most minor the SOPHiA DDM™ Platform updates do not affect the variant detection in commercial panels and are not included in Non-Regression Reports.
Upon request, Non-Regression Reports for validated solutions are provided by SOPHiA GENETICS and are sent by email by Subject Matter Experts (SME) or sales representatives.
The SOPHiA DDM™ Platform updates are not released until all known issues are addressed. The Non-Regression Reports that are sent reflect the final optimized status of variant detection.
The acceptance standard for a Non-Regression Report is that the overall variant detection sensitivity in any validated solution (measured on all confirmed reference variants) must be unchanged or increased. Any changes in the detection of reference variants are analysed in detail and are explained by bioinformatics experts in the report.
Diseases can easily be added when a new interpretation project is created.

1) After selecting “Add interpretation”, a new window will appear.

2) In the pop-up window, a new link “select” is available.

3) Once clicked, the Disease Ontology tree opens to select a disease.

This disease tree can be searched or alternatively, you can start typing the respective disease name. The matching disorders are then immediately highlighted in the list.

TIPs:

– You can bookmark regularly selected diseases by clicking the “star” icon next to the disease.

– In cases where several diseases should be selected simultaneously, hold CTRL (MAC: cmd) while selecting several diseases.

For detailed instructions please refer to the Operation Manual Section 3.11.3.


Figure 1: Select a disease from any level of the ontology tree.

SEARCH FOR A DISEASE

The disease tree can be easily searched. Start typing the respective disease name. The list of terms containing the substring is instantly updated and resulting disorders are highlighted in the list.

BOOKMARK A DISEASE

Users can bookmark often selected diseases by clicking the “star” icon next to the disease. Alternatively, users can click Shift + B (Fig. 2). In both cases, the disease then appears and is saved in the list of bookmarks above the tree.


Figure 2: Bookmark a disease by clicking the “star” icon or pressing Shift + B.

MULTI-SELECT DISEASES

Several diseases can be selected and added to an interpretation project by clicking CTRL (Mac: cmd) while selecting them (Fig. 3).


Figure 3: Multi-select several diseases. Click to select a disease, hold CTRL pressed, select one or more other diseases. Click OK to confirm. All diseases are added to the interpretation project.
The SOPHiA DDM™ Platform evaluates the impact of each variant on all transcripts of the gene in the kit. For each variant, the platform displays the transcript for which it is most deleterious. The variant annotation (coding consequence, cDNA HGVS nomenclature, protein effect) is then provided relative to this transcript. This method is used to minimize the probability of missing a potentially pathogenic mutation.
During variant analysis, the SOPHiA DDM™ Platform has the capability to directly ascertain the status of a pre-defined set of features.

This set of features can be defined both at the genomic and protein level. The monitoring of a given genomic region, such as an exon, is also possible.

If the screening analysis is enabled, a screening panel displaying the status of the pre-defined features is available on SOPHiA DDM™ Platform.

The pre-defined set is thoroughly tested before being deployed. This procedure ensures that all events are assessable from the product used. Therefore, this analysis can only be enabled by our team.
Using PMS2 and PMS2CL as an example: For both gene and pseudogene, exons 11-15 are very similar and the SOPHiA DDM™ Platform provides a warning for all variants that are detected in PMS2 exons 11-15 according to one of 3 categories:

Pseudogene_identical

applies to all variants detected in PMS2 exon 15, which are identical to the corresponding exon in PMS2CL. This warning indicates that it is impossible to distinguish a variant listed for PMS2 exon 15 from a variant that may be located in the corresponding exon in pseudogene PMS2CL.

Pseudogene_polymorphism

applies to variants detected in PMS2 exon 13 or exon 14, which are very similar to the corresponding exons in PMS2CL and – importantly – where gene conversion is frequently observed. These PMS2 exons may have replaced the corresponding sequence in the PMS2CL pseudogene locus or vice versa. Although PMS2 and PMS2CL sequences can be distinguished in these exons, no high confidence conclusions can be made since the observed PMS2 sequence (and any variants detected in this context) could originate either from the PMS2 gene locus or the PMS2CL pseudogene locus (due to gene conversion).

Pseudogene_distinct

applies to variants detected in PMS2 exon 12 or exon 11, which are very similar to the corresponding exons in PMS2CL but where gene conversion is rare. Here, the PMS2 sequence can be distinguished from the corresponding PMS2CL sequence with reasonable confidence [e.g. the observed PMS2 sequence (and any variants detected in this context) actually originates from PMS2 and not the PMS2CL pseudogene].
There are two different BRCA1 exon naming conventions.

1) The BRCA1 legacy exon nomenclature is still used by some clinical databases, where exon 4 is missing due to an initial oversight during BRCA1 protein characterization – all following exons have a number increased by one.

2) The standard HGVS/RefSeq/Ensembl nomenclature ranks exons dependent on the specific transcript (NM_*, ENST*) and they are always numbered in increasing order from 1 up to the last exon.
During variant analysis, the SOPHiA DDM™ Platform has the capability to directly ascertain the status of a pre-defined set of features.

This set of features can be defined both at the genomic and protein level. The monitoring of a given genomic region, such as an exon, is also possible.

If the screening analysis is enabled, a screening panel displaying the status of the pre-defined features is available on SOPHiA DDM™ Platform.

The pre-defined set is thoroughly tested before being deployed. This procedure ensures that all events are assessable from the product used. Therefore, this analysis can only be enabled by our team.
This warning only affects amplicon-based target enrichment.

The presence of a variant in the region where the primer anneals can affect binding affinity, and low binding affinity can affect the PCR amplification. The end result is that for this particular allele, the entire amplicon may not amplify, and hence ”allele dropout” (no longer visible in the data). Only sequences from reads from the allele without the variant would be present in the data. If there are any other variants on the same allele (as the variant causing the allele drop-out) and they are not covered by other amplicons, they will be missed.

Variants that can cause an allele drop-out will only be detected if there are other amplicons overlapping the primer region. The end primer of the last amplicon of a target region or the primers of standalone amplicons will not be covered, so the SOPHiA DDM™ Platform can not provide a warning for potential drop-outs in such amplicons.

TIP: In such cases, the relative coverage for that amplicon would be lower. If CNV analysis is available for a given panel, it can confirm an allele drop-out in the same region as a deletion / undetermined status. If there is no CNV result, we recommend comparing the relative coverage of this amplicon across several samples. The majority of SNVs will likely not cause an issue, since many enrichment kit providers include common polymorphisms in their primers. In the case of INDELs, an allele drop-out is very probable, and we recommend confirming the region with another method (e.g. Sanger sequencing.)

SOPHiA DDM™ – IT Technical issues (server, connections)

For detailed instructions please refer to the Operation Manual Section 1.1.
The most common reason is that your Proxy or Firewall is blocking the SOPHiA GENETICS servers. This can be easily fixed by your IT department by updating your firewall rules to allow access to all our servers.

It seems like your network system (proxy or firewall) blocks access to our servers.

Firewall rules
In case you are using a firewall, your IT team must update the firewall rules to open access to our servers.

All in secure mode (https) and port 443:
allow connections for all sophia sub domains : *.sophiagenetics.com.
ddm-fra.sophiagenetics.com
ddm-aus.sophiagenetics.com
ddm-br.sophiagenetics.com
ddm-us.sophiagenetics.com
ddm-ch.sophiagenetics.com
ddm-cha.sophiagenetics.com
dropgen.sophiagenetics.com
apis-cha.sophiagenetics.com
iam.sophiagenetics.com
uploaderstoragecha.blob.core.windows.net
lma.sophiagenetics.com/sgml_sga_1/ddm
www.ncbi.nlm.nih.gov/SNP
www.ncbi.nlm.nih.gov/clinvar
www.broadinstitute.org
exac.broadinstitute.org
cancer.sanger.ac.uk/cosmic
www.internationalgenome.org
evs.gs.washington.edu/EVS
127.0.0.1:21000
127.0.0.1:60151
ddm-aus.sophiagenetics.com

Proxy configuration

In case you are using a proxy, your IT department must do the following:
SOPHiA DDM™ will try to detect automatically your proxy configuration based on the computer’s network settings.
Unfortunately, some proxies cannot be properly identified, and you might encounter problems connecting. If this happens, you can define the proxy configuration and force SOPHiA DDM™ to use it.
If you need to go through the proxy, you can create a file in c:\Users\”pc_username”\.dg-cache\proxyCfg.txt with the following example:
-Dhttps.proxyHost=10.165.3.76
-Dhttps.proxyPort=8080
-Dhttp.proxyHost=10.165.3.76
-Dhttp.proxyPort=8080
Please keep in mind that the above proxy is just an example, as we do not know which IP you’re using. Also, please ensure that you add the – right before D.
In case the proxy also needs authentication, the user will be prompted upon startup to enter the credentials.
There could be multiple reasons for a delay.

1) You may have closed your PC before the upload was completed, logged out of your account or removed the files from their original saved location. When a run is uploaded, it is associated to a specific, computer, Windows user and the SOPHiA DDM™ Platform user.

– To resume upload, simply restart the SOPHiA DDM™ Platform and log in.

– This should be done on the same computer and using the same windows user and the same SOPHiA DDM™ Platform user credentials that started the run.

– Using other credentials will not work.

2) If you are using a USB key, please make sure not to remove it before the copy is completely finished.

3) If you are using a network folder, make sure the folder is always available during the upload. We recommend making a local copy of the files before starting an upload.

4) If you still don’t see the upload bar moving on the bottom, please contact support using the “Contact support” link button in the SOPHiA DDM™ main page.
This is directly related to the speed of your internet connection. All the data is downloaded/uploaded on the SOPHiA DDM™ Platform directly from SOPHiA GENETICS Servers and not stored locally. A slow internet connection directly affects performance.

– We recommend a minimum of 10 Mb/s upload speed.

– If you upload large panel files, we recommend a minimum of 20 Mb/s upload speed.

– Please consult your IT department to improve your internet speed limit.

You can use one of the Speed Test links below to confirm your speed connection with our servers based on your location:

Swiss Server
French Server
Australian Server
Netherland Server
US Server
Brasilian Server
Canadian Server
Turkish Server

Alamut™ Visual Plus

gnomAD data are displayed in the Allele Frequency Databases track. Right-clicking on the variant of interest reveals the variant panel with details from gnomAD shown in a box within AlamutTM Visual Plus. At the top of the gnomAD box you will see Genome and Exome tabs, which, if selected, are highlighted in blue and information is shown in the box. Clicking on the hyperlinked “gnomAD (vx.x.x)” tab will take you to the gnomAD page for the variant. Note that depending on the type of data and which filters are used, the text will be different, but it will start with gnomAD.
AlamutTM Visual Plus currently uses Genome Aggregation Database Version 2.1 for GRCh37/hg19 genome build. The link to this version of gnomAD is: https://gnomad.broadinstitute.org/variant/1-55516888-G-GA?dataset=gnomad_r2_1 .
For GRCh38/hg38 genome build, gnomAD 4.0.0 is now available in AlamutTM Visual Plus. The link to this version of gnomAD is: https://gnomad.broadinstitute.org/variant/1-55051215-G-GA?dataset=gnomad_r4
With 730,947 exomes and 76,215 genomes, the gnomAD v4.1 call set is the largest of all the gnomAD versions. It also contains nearly all data from prior versions (v2 and v3) except a small number of samples excluded due to data quality and updated sample filtering pipelines. Since gnomAD v4.1 is mapped to GRCh38, if you haven’t switched to GRCh38, now is the time!
RefSeq displays GRCh38 as a default genome for all rsID links. On dbSNP variant pages, links for the GRCh37 genome build are usually available.
Two different conventions are used for exon naming.
Systematic Exon Numbering starts at 1 and counts and numbers each exon numerically. So, if there are 10 exons, for example, they will be numbered from 1 to 10.
Whereas Custom Exon Numbering is the historical numbering that was determined when the gene was first sequenced. The Custom Exon Numbering originally included splicing variants. For example, if there was a splicing difference, you could have an exon numbered 10a in one transcript and 10b in another transcript. With Systemic Exon Numbering, this exon would just be numbered 10 if it was the 10th exon. The Custom Exon Numbering usually comes from the original paper and/or the scientist that determined the sequence. This had previously been supported by NCBI but was discontinued several years ago in favor of Systematic Exon Numbering. However, researchers still use Custom Exon Numbering for genes such as BRCA1 and BRCA2.
In the AlamutTM Visual Plus toolbar, if you click on “Exon Naming” you can change between the two naming conventions. Alternatively, you can set the program to “use systematic exon numbering by default”. To do this, open the “Alamut Visual Plus” menu > “Preferences”, and then select this setting in the “View” tab.
Ensembl and RefSeq transcripts differ in that Ensembl transcripts are mapped onto the reference genome, whereas RefSeq transcripts are mapped onto mRNA sequences. Due to differences between reference genomes and individual mRNAs, some RefSeq mRNA’s might not map perfectly to the reference genome, resulting in the possibility of small differences between Ensembl and RefSeq transcripts. AlamutTM Visual Plus uses Splign (a tool developed by RefSeq) to align all transcripts to the genome build.
For more information please see: https://www.ensembl.org/Help/Faq?id=294
The user manual section on splicing (from pg 63) provides an overview of this topic, as well as links to publications and more splicing-related information: https://extranet.interactive-biosoftware.com/User%20Guide%20Alamut%20Visual%20Plus%20v1.6.1.pdf

The following paper also provides a good overview of this topic: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC275472/
The first sentence of the abstract states: “Cryptic splice sites are used only when use of a natural splice site is disrupted by mutation.”

Overall, the literature suggests that both natural and cryptic splice sites are important, but their relevance depends on the context of your investigation.
SIFT scores are calculated based on multiple sequence alignments of protein orthologues (SIFT Aligned Sequences). Scores differ between builds 37 and 38, because MSA (Multiple sequence Alignments) can be different depending on the genome used. MSA are accessible by clicking on the missense prediction tool button in the Variant Panel. PolyPhen-2 (in AlamutTM Visual Plus) does not use MSA, just the human protein sequence and the substitution information.
The following page explains possible reasons for inconsistencies between versions: http://genetics.bwh.harvard.edu/pph2/dokuwiki/faq. Our team is continuously working to harmonize and update the protein sequence database used to build the multiple sequence alignment by PolyPhen-2. This could explain the differences in the scores.
SIFT scores are calculated based on multiple sequence alignments of protein orthologues (SIFT Aligned Sequences). The scores can differ between genome builds 37 and 38 because MSA (Multiple sequence Alignments) can differ depending on the genome used. Differences seen between the AlamutTM Visual Plus in-house predictors and the predictor website can be because the MSA differs and/or because the algorithm version differs between the website and the in-house versions.
The Orthologue Alignments for each gene are downloaded from Ensembl Compara (https://www.ensembl.org/info/genome/compara/index.html). Differences between GRCh37 and GRCh38 are due to the species used in the alignments not being the same in all cases. The data for GRCh38 are more up to date in Ensembl, but it is extremely difficult to determine which alignment is better. AlamutTM Visual Plus includes a standard set of species, but this is dependent on what is available in Ensembl Compara. These sequence alignments are used for missense predictions in AlamutTM Visual Plus.
Users do not have direct access to the AlamutTM Visual Plus database and thus do not need to write SQL queries to access the data. All relevant data are available through the AlamutTM Visual Plus interface by clicking on different tabs.
Several RefSeq transcript versions cannot be added to the AlamutTM Visual Plus database, due to significant mismatches with the reference genomes GRCh37 and GRCh38. AlamutTM Visual Plus uses Splign (https://www.ncbi.nlm.nih.gov/sutils/splign/splign.cgi), a RefSeq alignment algorithm, to ensure that transcripts align successfully with the reference genomes before adding them into the AlamutTM Visual Plus reference database.
The nucleotide conservation track shows scores of evolutionarily conserved nucleotides based on phylogenetic studies between species. Nucleotide conservation scores are extracted from PhastCons statistical algorithms, represented by grey color. The red color means that the indicated value of one nucleotide is higher than what the height of the stick symbolizes. The threshold is fixed at 4 (as for the UCSC). These colors are visible when viewing values in the tooltip.
AlamutTM Visual Plus applies the internationally recognized HGVS nomenclature. In the case of reverse genes, the 3’ rule is applied. For all descriptions, the most likely 3’ position of the reference sequence is arbitrarily assigned to have been changed. The 3’ rule also applies to changes in single residue stretches and tandem repeats (nucleotide or amino acid). The 3’ rule applies to ALL descriptions (genome, gene, transcript, and protein) of a given variant. See: http://varnomen.hgvs.org/recommendations/general/
The export of external annotation is allowed per variant from the Variant Panel or directly from the ‘Variant Exporter’ window.
If the sequencer used to generate the ab1 files is Applied Biosystem 3130XL / ABI3130XL:

To load a Sanger file, AlamutTM Visual Plus uses the “PBAS” tag of the ABIF format. The “PBAS” tag contains the base-called sequence (i.e the nucleic sequence identified from the electropherogram).

Applied Biosystem 3130XL / ABI3130XL sequencer does not do the base calling step. In that case, sequence analysis software has to be used to do the base calling and to generate a Sanger file compatible with AlamutTM Visual Plus.

See here for more info about the ABIF format: https://projects.nfstc.org/workshops/resources/articles/ABIF_File_Format.pdf
By default, AlamutTM Visual Plus includes all available variants from GnomAD, irrespective of the filters or the filter cut-off.

There are three filters – the “PASS”, RF, and AC0 filters. Each of these filters has cut-offs defined by gnomAD, for both Exome and Genome data. If you only want to view variants that have passed through all the quality filters, tick the box “PASS only” on the gnomAD track for AlamutTM Visual Plus, deciding if you want to see Exome or Genome data, or both.
gnomAD links are built based on the position, ref, and alt of the variant. In AlamutTM Visual Plus, if we apply the 3′ rule, those values may change, which directly affects the url functionality.
We compute Genotype Count based on values from a gnomAD VCF. In some cases, we end up with negative counts due to discrepancies in the initial values provided in the VCF. This is the way gnomAD is handling these variants.
This is due to occasional genome/transcript sequence discrepancies, where the genome reference includes polymorphism minor alleles, but the transcript includes corresponding major alleles. This means that some genomic variants are seen as ‘non-variants’ if analyzed at the transcript level.

Basically, at the positions highlighted in red, the nucleotide of the transcript differs from the nucleotide of the genome build (GRCh37 or GRCh38). For these nucleotides, it is more difficult to definitively determine whether a variant is indeed a variant.

These discrepancies mainly occur in RefSeq transcripts (Beginning with “NM_”), as RefSeq does not correct the transcript to the genome build, while ENSEMBL transcripts (beginning with “ENST”) are corrected to match the nucleotides present in the genome build.
Two different conventions are used for exon naming.
Systematic Exon Numbering starts at 1 and counts and numbers each exon numerically. So, if there are 10 exons, for example, they will be numbered from 1 to 10.
Whereas Custom Exon Numbering is the historical numbering that was determined when the gene was first sequenced. The Custom Exon Numbering originally included splicing variants. For example, if there was a splicing difference, you could have an exon numbered 10a in one transcript and 10b in another transcript. With Systemic Exon Numbering, this exon would just be numbered 10 if it was the 10th exon. The Custom Exon Numbering usually comes from the original paper and/or the scientist that determined the sequence. This had previously been supported by NCBI but was discontinued several years ago in favor of Systematic Exon Numbering. However, researchers still use Custom Exon Numbering for genes such as BRCA1 and BRCA2.
In the AlamutTM Visual Plus toolbar, if you click on “Exon Naming” you can change between the two naming conventions. Alternatively, you can set the program to “use systematic exon numbering by default”. To do this, open the “Alamut Visual Plus” menu > “Preferences”, and then select this setting in the “View” tab.
AlamutTM Visual Plus includes a splicing module accessible from the variant panel that integrates a number of prediction algorithms. It provides the user with automatically-computed prediction scores.
A brief description of splicing signal prediction can be found on page 63 available here: https://extranet.interactive-biosoftware.com/User%20Guide%20Alamut%20Visual%20Plus%20v1.6.1.pdf

AlamutTM Visual Plus computes splicing scores based on implemented algorithms. The user is responsible for interpreting these scores based on the scientific context and peer-reviewed guidelines.
We can suggest the following paper to help you interpret splicing scores: https://pubmed.ncbi.nlm.nih.gov/22505045/

And the followingvideo from ClinGen: https://clinicalgenome.org/tools/educational-resources/materials/splicing-and-in-silico-splicing-predictors/
SHANK3 is quite a problematic gene. AlamutTM Visual Plus mapped the available transcripts to genome builds and NM_033517.1 could not be mapped to either genome build, due to mismatches between the transcript and builds.

We do, however, have a RefSeqGene NG_008607.2, which is based on the transcript NM_033517.1
SIFT 6.2.0
Polyphen2
The Polyphen-2 prediction scores automatically displayed in AlamutTM Visual Plus are extracted from the WHESS database (http://genetics.bwh.harvard.edu/pph2/dbsearch.shtml). The WHESS database contains a pre-computed set of PolyPhen-2 predictions for the Whole Human Exome Sequence Space.
Whereas, the scores obtained on the site using the batch query (http://genetics.bwh.harvard.edu/pph2/bgi.shtml) are generated upon each request.
In Alamut Visual and AlamutTM Visual Plus, the SIFT missense predictors are computed using the orthologues alignment. Differences in SIFT scores can be explained by differences in orthologue alignment. Alamut Visual contains ‘in-house’ orthologues for some genes, whereas AlamutTM Visual Plus contains Ensembl orthologues.
In Alamut Visual and AlamutTM Visual Plus, the SIFT missense predictors are computed using the orthologues alignment. Differences in SIFT scores can be explained by differences in orthologue alignment. Alamut Visual contains ‘in-house’ orthologues for some genes, whereas AlamutTM Visual Plus contains Ensembl orthologues.
Alamut Visual uses conservation scores from UCSC:

 https://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=cons46way, whereas, AlamutTM Visual Plus uses a more up-to-date set of conservation scores.

Alamut Visual
PhastCons
GRCh37 – 46 vertebrates (2013)
GRCh38 – 100 vertebrates (2013)
AlamutTM Visual Plus
PhastCons
GRCh37 – 100 vertebrates (2018)
GRCh38 – 100 vertebrates (2018)
In AlamutTM Visual Plus, the limit fixed to trigger NMD is 50 nucleotides.
This is related to mismatches that can exist between the transcript and reference genome. Alamut Visual only uses transcript data, while AlamutTM Visual Plus displays both transcript and reference genome sequences.
It is possible to click on the “Alamut” button in the SOPHiA DDMTM Platform, or to download BAM files from WES (or from targeted panels or even from whole-genome sequencing). In AlamutTM Visual Plus, the BAM file will be loaded by segment and visualized by gene due to the index file (.bai) associated with the BAM file. The BAM file will not be entirely loaded for all genes at once, meaning that the BAM file can be of any size.
It is not possible to have more than one transcript related to a single variant in the same database. A new database will need to be created for a different transcript.
When creating a new variant in an intergenic area, AlamutTM Visual Plus looks for the two closest genes (upstream and downstream). If no gene is found in a 10,000,000-nucleotide area around the variant position, the “No nearby genes are available” message is displayed.
It is not recommended to install AlamutTM Visual Plus on a shared drive because of performance issues and potential application instability. The best way to install AlamutTM Visual Plus is:

- Run AlamutTM Visual Plus executable (.exe) locally and do not store it on a shared drive.
- When installing the application, select a local settings folder.
- To share variant databases, one of your users can create a new Local 
- Variant Database from the menu and choose to store it on a shared drive. - This database can later be imported by any user that has access to this location.
- If a database is stored on a shared drive or disk, it should be flagged as “Shared Database”.
Catalogs are updated at the same frequency as the release of those catalogs. For example, Clinvar is updated bi-monthly, whereas catalogs such as dbSNP are updated yearly, and other catalogs less frequently. GnomAD is updated when a release is available that will cover all AlamutTM Visual Plus requirements.

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