## Highlights

- •MPSproto requires calibration of stutters and noise.
- •MPSproto increases the sensitivity of MPS-STR mixture analyses.
- •MPSproto is a good alternative to EuroForMix.
- •The gamma model in MPSproto performed best overall.

## Abstract

## Keywords

## 1. Introduction

- Barrio P.A.
- et al.

*Forensic Sci. Int. Genet.*2020; 49https://doi.org/10.1016/j.fsigen.2020.102391

- Benschop C.C.G.
- et al.

*Forensic Sci. Int. Genet.*2021; 52https://doi.org/10.1016/j.fsigen.2021.102489

- Li R.
- et al.

*Forensic Sci. Int. Genet.*2020; 45https://doi.org/10.1016/j.fsigen.2019.102225

- Li R.
- et al.

*Forensic Sci. Int. Genet.*2020; 45https://doi.org/10.1016/j.fsigen.2019.102225

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Li R.
- et al.

*Forensic Sci. Int. Genet.*2020; 45https://doi.org/10.1016/j.fsigen.2019.102225

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

## 2. Materials and methods

### 2.1 Data

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

### 2.2 The MPSproto model

#### 2.2.1 Models for sequence reads

for the GA model, and for the NB model we have:

*K*individuals (

*K*-person mixture), where each individual contributes with mixture proportion parameter ${\pi}_{k}\in \left[\mathrm{0,1}\right]$. ${n}_{m,a,k}$ is the allele contribution (0, 1 or 2) decided by the assumed genotypes vector ${\mathit{g}}_{m}=\left({g}_{1},{g}_{2},\dots ,{g}_{K}\right)$: ${n}_{m,a,k}=I\left({g}_{k,1}=a\right)+I\left({g}_{k,2}=a\right)$, where ${g}_{k}={g}_{k,1}/{g}_{k,2}$ and $I$ is the indicator function. The marker efficiency parameters $\mathit{A}={(A}_{1},\dots ,{A}_{M})$ are restricted such that ${\frac{1}{M}\sum A}_{m}$= 1. Formulae for the probability density and cumulative functions $f$ and $F$ of the read depths, and the derivation of the size argument are included in the Appendix B, Appendix B.

#### 2.2.2 Stutter model

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

where $*$ indicates the ${\mathrm{\psi}}_{m,a}$ identity without assuming any stutter model (Eq. 1 for the GA model or Eq. 2 for the NB model). The first term is the “donates” part whereas the second term is the “receives” part. When several stutter types ($t=1,\dots $) are defined, the formula extends to:

*.*The formula above hence gives:

where ${{\beta}^{m,t}=(\beta}_{0}^{m,t},{\beta}_{1}^{m,t},{\beta}_{2}^{m,t})$ are model coefficients of the regression and ${x}_{1,a}^{m,t}$ is the BLMM for stutter $a$ and ${x}_{2,a}^{m,t}$ is the BLMM for a different motif (also for stutter $a$). The latter is used for n0 stutters where two parts of the sequence stutter simultaneously, but there is no net change of the size of the stutter product compared to its parent allele.

#### 2.2.3 Noise model

- (A)The number of (unique) noise sequences, $k$, follows a geometric distribution with parameter $p$:$\mathit{Pr}(k\mathrm{|}p)=p{\left(1-p\right)}^{k}$
- (B)The read depth of a noise sequence, $y$, is proportional to a Type I Pareto distribution with parameter $\lambda $ [[22]]:$d\left(y|,\lambda \right)=c*\lambda {T}_{m}^{\lambda}{y}^{-(\lambda +1)}$

#### 2.2.4 Dropout and degradation model

#### 2.2.5 Implementation

### 2.3 Calibration of MPSproto

- 1)Estimation of marker amplification efficiencies
- 2)Identification of stutter types and estimation of regression coefficients
- 3)Estimation of the noise model

#### 2.3.1 Estimating marker amplification efficiency

**,**the read depth sum per marker per sample is used. For each calibration sample $s=1,\dots ,S$, the sum of reads per marker is calculated as ${y}_{\mathrm{s},\mathrm{m}}={\mathrm{\Sigma}}_{\mathrm{a}}{y}_{\mathrm{s},\mathrm{m},\mathrm{a}}$. If ${y}_{s,m}\ge {T}_{m}$ (no marker dropout), we assume that the sum is gamma distributed (for the GA model), with $2{{A}_{m}\omega}_{s}^{-2}$ as the shape argument and ${\mu}_{s}{\omega}_{s}^{2}$ as the scale argument. On the other hand, if ${y}_{s,m}<{T}_{m}$ (marker dropout is present), then the dropout probability is calculated as

where ${F}_{s,m}$is the cumulative density function of the gamma distribution (applied with same shape and scale argument, see Appendix). The estimated marker amplification efficiency parameters are obtained by maximizing the likelihood

#### 2.3.2 Stutter calibration

- •BW1: Backward stutter (
*n-1*) where the longest repeat is stuttering. - •FW1: Forward stutter (
*n+1*) where the longest repeat is stuttering. - •DBW1: Double backward stutter (
*n-2*) where the longest repeat is stuttering. - •BW2: Backward stutter (
*n-1*) where a repeat other than the longest repeat is stuttering. - •FWBW: Longest repeat produces forward stutter,
**and**a repeat other than the longest repeat produces backward stutter (*n0*) - •BWFW: Longest repeat produces backward stutter,
**and**a repeat other than the longest repeat produces forward stutter (*n0*)

*getStutteredSequence*is applied on all donor alleles and compared with artefacts. Stutter artefacts are not considered in the calibration if multiple parental allele candidates are observed.

^{ 1}

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

#### 2.3.3 Estimating noise model

- (A)Let the number of noise sequences per sample be given as ${x}_{1},{x}_{2},\dots ,{x}_{S}$. An additional observation is always appended as ${x}_{S+1}=1+\mathrm{max}\left({x}_{1},\dots ,{x}_{S}\right)$ to make the model more robust for new observations. The $p$ parameter is then estimated as $\stackrel{\u02c6}{p}={\left(\stackrel{\u0305}{x}+1\right)}^{-1}$, where $\stackrel{\u0305}{x}$ is the mean of the observations.
- (B)Let the read depth of the noise sequences be given vectorized as ${y}_{1},{y}_{2},{\dots ,y}_{n}$. An additional observation is always appended as ${y}_{n+1}={T}_{m}$ to make the model more robust for new observations. If $n=0$ (no observations) two additional observations, ${y}_{n+2}={T}_{m}$ and$\phantom{\rule{1em}{0ex}}{y}_{n+3}={T}_{m}+1$, are appended to enable the model to be fitted. The $\lambda $ parameter is then estimated using the maximum likelihood estimate, obtained as $\stackrel{\u02c6}{\lambda}$.

#### 2.3.4 Implementation and functionalities

*calibrateModel*in MPSproto. This eases the calibration steps for users. Additionally, a tutorial is available at https://github.com/oyvble/MPSproto/blob/master/doc/MPSproto_tutorial.html.

### 2.4 Inference of MPS evidence and LR calculations

#### 2.4.1 Definition of the likelihood function and the calculation of LR

**,**whereas the stutter model parameters, marker amplification efficiency and noise model are based on the calibration. As with EuroForMix, the LR calculation is based on optimizing the likelihood function over the model parameters for a specific proposition $H$:

where for a given marker $m$, ${E}_{m}$ is the evidence information (sequences and read depths), and ${\mathit{g}}_{\mathit{m}}=({g}_{1},\dots ,{g}_{K})$ is the joint genotype combination of $K$ contributors, traversed over the joint genotype outcome ${G}_{m}^{H}$ defined from proposition $H$. More specifically:

where $k$ is the assumed number of noise sequences for a given genotype combination (not derived from a contributor or a stutter). If a sequence $a$ is assumed to be a noise sequence, then $f\left({y}_{m,a}|,\theta ,{\mathit{g}}_{\mathit{m}}\right)=d({y}_{m,a}|{\stackrel{\u02c6}{\lambda}}_{m})$

**.**The calculation of the genotype probabilities, $\mathit{Pr}({\mathit{g}}_{\mathit{m}}\mathit{|H})$, includes the ${F}_{\mathit{st}}$coancestry coefficient to take sub-population structure into account, with formulae described in [

#### 2.4.2 Mixture evaluation study

- -${H}_{p}$: “POI + ($K-1$) unknown unrelated are contributors”
- -${H}_{d}$: “$K$ unknown unrelated are contributors”

- -${H}_{p}$: “POI + Ref1 + 2 unknown unrelated are contributors”
- -${H}_{d}$: “Ref1 + 3 unknown unrelated are contributors”

- 1.MPSproto applied with gamma model (GA)
- 2.MPSproto applied with negative binomial model (NB)
- 3.EuroForMix (EFM)

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

#### 2.4.3 Model comparisons

*delong*).

*validMLE*(MPSproto) or

*validMLEmodels*(EuroForMix). A small p-value (e.g. <0.01) from the test indicates a non-adequate model for the read depth.

## 3. Results

### 3.1 Calibration of MPSproto for the mixture evaluation study

Marker | A | p | lambda | BW1 | FW1 | DBW1 | BW2 | FWBW | BWFW |
---|---|---|---|---|---|---|---|---|---|

CSF1PO | 0.324 | 0.989 | 15.6 | -4.44/0.133 | NA | NA | NA | NA | NA |

D10S1248 | 0.629 | 0.91 | 3.85 | -3.91/0.114 | NA | -3.83 | NA | NA | NA |

D12S391 | 0.574 | 0.623 | 3.13 | -3.23/0.123 | NA | -5.47/0.139 | -4.99/0.227 | -3.78 | NA |

D13S317 | 0.822 | 0.85 | 4.08 | -5.73/0.201 | -4.02 | NA | NA | NA | NA |

D16S539 | 1.14 | 0.765 | 3.13 | -3.79/0.147 | -4.4 | -7.06/0.246 | NA | NA | NA |

D17S1301 | 0.543 | 0.989 | 15.6 | -4.25/0.2 | NA | -3.87 | NA | NA | NA |

D18S51 | 0.968 | 0.778 | 2.41 | -3.69/0.101 | -6.74/0.16 | -6.02/0.11 | NA | NA | NA |

D19S433 | 1.12 | 0.204 | 3.55 | -3.74/0.102 | NA | NA | NA | NA | NA |

D1S1656 | 0.305 | 0.867 | 4.26 | -4.3/0.17 | NA | NA | NA | NA | NA |

D20S482 | 2.44 | 0.867 | 3.12 | -2.13 | -3.97 | -4.87 | NA | NA | NA |

D21S11 | 0.533 | 0.746 | 3.87 | -3.54/0.0843 | -3.61 | NA | -3.36 | NA | NA |

D22S1045 | 1.44 | 0.948 | 6.04 | -3.6/0.118 | -5.57/0.146 | -6.2/0.136 | NA | NA | NA |

D2S1338 | 1.21 | 0.446 | 1.85 | -2.63/0.0505 | NA | -5.37/0.0982 | -6.92/0.427 | -4.38 | -4.37 |

D2S441 | 1.14 | 0.901 | 3.39 | -3.56 | -4.9 | NA | NA | NA | -4.31 |

D3S1358 | 2.08 | 0.286 | 2.43 | -4.15/0.127 | -6.85/0.177 | -7.05/0.178 | NA | -4.69 | -4.83 |

D4S2408 | 0.993 | 0.892 | 4.01 | -5.27/0.208 | -6.86/0.23 | NA | NA | NA | NA |

D5S818 | 0.29 | 0.968 | 12.4 | -2.85 | NA | NA | NA | NA | NA |

D6S1043 | 1.09 | 0.85 | 3.03 | -3.67/0.1 | -7.42/0.259 | -4.59 | NA | NA | NA |

D7S820 | 0.791 | 0.467 | 2.24 | -5.17/0.222 | -4.35 | NA | NA | NA | NA |

D8S1179 | 1.48 | 0.875 | 3.21 | -3.39/0.122 | -6.83/0.217 | -6.3/0.178 | NA | NA | NA |

D9S1122 | 1.55 | 0.812 | 3.46 | -4.77/0.207 | -4.19 | -7.53/0.231 | NA | NA | NA |

FGA | 1.05 | 0.655 | 3.56 | -3.03/0.0873 | -5.78/0.112 | -5.79/0.135 | NA | NA | NA |

PENTA E | 0.256 | 0.968 | 6.17 | -2.5 | NA | NA | NA | NA | NA |

TH01 | 2.77 | 0.728 | 2.97 | -4.19/0.193 | -4.81 | -5 | -5.25 | NA | NA |

TPOX | 0.894 | 0.938 | 3.81 | -5/0.167 | NA | NA | NA | NA | NA |

VWA | 0.382 | 0.958 | 3.09 | -3.56/0.12 | NA | NA | NA | NA | NA |

PENTA D | 0.181 | 0.431 | 2.83 | -5.7/0.189 | NA | NA | NA | NA | NA |

**A**is the estimated marker amplification efficiency,

**p**is geometrical model parameter for the number of noise alleles and

**lambda**is pareto model parameter for the noise size. The remaining columns indicate the stutter coefficients (intercept and slope) of the different stutter types separated by a backslash. Slope was only included if significant. “NA” means that the stutter type was not modelled.

### 3.2 LR for ${H}_{p}\phantom{\rule{1em}{0ex}}$true comparisons

### 3.3 ROC analysis and LR for ${H}_{d}\phantom{\rule{1em}{0ex}}$true comparisons

Thresh | Model | TPR | FPR | FN | FP |
---|---|---|---|---|---|

1 | EFM (T = 11) | 0.931 | 0.0238 | 11 | 10 |

1 | EFM (T = 20) | 0.969 | 0.0548 | 5 | 23 |

1 | EFM (T = 30) | 0.938 | 0.0905 | 10 | 38 |

1 | NB | 0.938 | 0.0857 | 10 | 36 |

1 | GA | 0.956 | 0.157 | 7 | 66 |

10 | EFM (T = 11) | 0.906 | 0 | 15 | 0 |

10 | EFM (T = 20) | 0.912 | 0 | 14 | 0 |

10 | EFM (T = 30) | 0.869 | 0 | 21 | 0 |

10 | NB | 0.906 | 0.00714 | 15 | 3 |

10 | GA | 0.944 | 0 | 9 | 0 |

### 3.4 Goodness of fit

### 3.5 Reducing the analytical threshold for EuroForMix

## 4. Discussion

### 4.1 Summary and overall findings

- Agudo M.M.
- Aanes H.
- Roseth A.
- Albert M.
- Gill P.
- Bleka Ø.

*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728

*calibrateModel*function in the software is used to perform all necessary calibration steps.

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Bleka Ø.
- Just R.
- Le J.
- Gill P.

*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319

- Benschop C.C.G.
- et al.

*Forensic Sci. Int. Genet.*2021; 52https://doi.org/10.1016/j.fsigen.2021.102489

### 4.2 Model considerations

#### 4.2.1 Stutters

#### 4.2.2 Noise

- Vilsen S.B.
- Tvedebrink T.
- Mogensen H.S.
- Morling N.

*Forensic Sci. Int. Genet. Suppl. Ser.*2015; 5: e416-e417https://doi.org/10.1016/j.fsigss.2015.09.165

- Benschop C.C.G.
- et al.

*Forensic Sci. Int. Genet.*2021; 52https://doi.org/10.1016/j.fsigen.2021.102489

#### 4.2.3 Thresholds and data filtering

#### 4.2.4 Marker amplification efficiencies

#### 4.2.5 Degradation

#### 4.2.6 Data format

- Jønck C.G.
- Qian X.
- Simayijiang H.
- Børsting C.

*Forensic Sci. Int. Genet.*2020; 48102331https://doi.org/10.1016/j.fsigen.2020.102331

#### 4.2.7 Model differences

### 4.3 Current implementation and future work

- Benschop C.C.G.
- et al.

*Forensic Sci. Int. Genet.*2021; 52https://doi.org/10.1016/j.fsigen.2021.102489

## 5. Conclusion

## Conflict of interest

## Acknowledgements

## Appendix A

### Mathematical description of the distributions

**Gamma distribution:**

**Negative binomial distribution** (re-parameterized version):

### Deriving the relation between size parameter and the coefficient of variation

**Computational sidenote**: Very large values of $\eta $ can cause computational issues which is solvable by restricting it below a value ${\eta}_{0}$. It can be derived that the restriction $\eta <{\eta}_{0}$ is equivalent to $\mu >{\left({\omega}^{2}-\frac{1}{{\eta}_{0}}\right)}^{-1}$. Hence smaller values of $\omega $ would require a large value of $\mu $. We used a value of ${\eta}_{0}=1e4$ in the C++ function which avoids the maximum likelihood optimization to crash using parallelization with OpenMP.

## Appendix B. Supplementary material

Supplementary material

Supplementary material

## References

- EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts.
*Forensic Sci. Int. Genet.*2016; 21: 35-44https://doi.org/10.1016/j.fsigen.2015.11.008 - Probabilistic genotyping software: an overview.
*Forensic Sci. Int. Genet.*2019; 38: 219-224https://doi.org/10.1016/j.fsigen.2018.11.009 - ‘Massively parallel sequencing techniques for forensics: a review.
*Electrophoresis.*2018; 39: 2642-2654https://doi.org/10.1002/elps.201800082 - The first GHEP-ISFG collaborative exercise on forensic applications of massively parallel sequencing.
*Forensic Sci. Int. Genet.*2020; 49https://doi.org/10.1016/j.fsigen.2020.102391 - Current state-of-art of STR sequencing in forensic genetics.
*Electrophoresis.*2018; 39: 2655-2668https://doi.org/10.1002/elps.201800030 - Application of a probabilistic genotyping software to MPS mixture STR data is supported by similar trends in LRs compared with CE data’.
*Forensic Sci. Int. Genet.*2021; 52https://doi.org/10.1016/j.fsigen.2021.102489 - Massively parallel sequencing of short tandem repeats—Population data and mixture analysis results for the PowerSeq™ system.
*Forensic Sci. Int. Genet.*2016; 24: 86-96https://doi.org/10.1016/j.fsigen.2016.05.016 - ‘The interpretation of single source and mixed DNA profiles.
*Forensic Sci. Int. Genet.*2013; 7: 516-528 - Analysis of forensic DNA mixtures with artefacts.
*Appl. Stat.*2015; 64: 1-32 - Sequencing of 231 forensic genetic markers using the MiSeq FGx™ forensic genomics system – an evaluation of the assay and software.
*Null.*2018; 3: 111-123https://doi.org/10.1080/20961790.2018.1446672 - Characterizing stutter variants in forensic STRs with massively parallel sequencing.
*Forensic Sci. Int. Genet.*2020; 45https://doi.org/10.1016/j.fsigen.2019.102225 - Modelling allelic drop-outs in STR sequencing data generated by MPS.
*Forensic Sci. Int. Genet.*2018; 37: 6-12https://doi.org/10.1016/j.fsigen.2018.07.017 - Stutter analysis of complex STR MPS data.
*Forensic Sci. Int. Genet.*2018; 35: 107-112https://doi.org/10.1016/j.fsigen.2018.04.003 - Modeling allelic analyte signals for aSTRs in NGS DNA profiles.
*J. Forensic Sci.*2021; 66: 1234-1245https://doi.org/10.1111/1556-4029.14685 - A comprehensive characterization of MPS-STR stutter artefacts.
*Forensic Sci. Int. Genet.*2022; 102728https://doi.org/10.1016/j.fsigen.2022.102728 - Use of the LUS in sequence allele designations to facilitate probabilistic genotyping of NGS-based STR typing results.
*Forensic Sci. Int. Genet.*2018; 34: 197-205https://doi.org/10.1016/j.fsigen.2018.02.016 - An examination of STR nomenclatures, filters and models for MPS mixture interpretation.
*Forensic Sci. Int. Genet.*2020; 48102319https://doi.org/10.1016/j.fsigen.2020.102319 R. Mitchell , D. Standage, lusSTR. Bioforensics, 2021. (Online). https://github.com/bioforensics/lusSTR.

- Sequence-based U.S. population data for 27 autosomal STR loci.
*Forensic Sci. Int. Genet.*2018; 37: 106-115https://doi.org/10.1016/j.fsigen.2018.07.013 L.I. Moreno, T.R. Moretti, Short Tandem Repeat Genotypes of Samples From Eleven Populations Comprising the FBI’s Population Database, 1, 2019.

Ø. Bleka, LUSstrR. 2022 (Online).https://github.com/oyvble/LUSstrR.

- Characterization of the Pareto distribution through a model of underreported incomes.
*Econometrica.*1970; 38: 251-255 - ‘Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.
*Clin. Chem.*1993; 39: 561-577 - Modelling noise in second generation sequencing forensic genetics STR data using a one-inflated (zero-truncated) negative binomial model.
*Forensic Sci. Int. Genet. Suppl. Ser.*2015; 5: e416-e417https://doi.org/10.1016/j.fsigss.2015.09.165 - ‘FDSTools: a software package for analysis of massively parallel sequencing data with the ability to recognise and correct STR stutter and other PCR or sequencing noise’.
*Forensic Sci. Int. Genet.*2017; 27: 27-40https://doi.org/10.1016/j.fsigen.2016.11.007 S.B. Vilsen, Statistical Modelling of Massively Parallel Sequencing Data in Forensic Genetics, Aalborg University, Aalborg.

- Does the use of probabilistic genotyping change the way we should view sub-threshold data?.
*Null.*2017; 49: 78-92https://doi.org/10.1080/00450618.2015.1122082 - Establishing STR and identity SNP analysis thresholds for reliable interpretation and practical implementation of MPS gDNA casework.
*Forensic Sci. Int. Genet. Suppl. Ser.*2019; 7: 363-364https://doi.org/10.1016/j.fsigss.2019.10.013 - STRait Razor: a length-based forensic STR allele-calling tool for use with second generation sequencing data.
*Forensic Sci. Int. Genet.*2013; 7: 409-417https://doi.org/10.1016/j.fsigen.2013.04.005 - STRinNGS v2.0: Improved tool for analysis and reporting of STR sequencing data.
*Forensic Sci. Int. Genet.*2020; 48102331https://doi.org/10.1016/j.fsigen.2020.102331 - toaSTR: a web application for forensic STR genotyping by massively parallel sequencing.
*Forensic Sci. Int. Genet.*2018; 37: 21-28https://doi.org/10.1016/j.fsigen.2018.07.006 - Massively parallel sequencing of forensic STRs: considerations of the DNA commission of the International Society for Forensic Genetics (ISFG) on minimal nomenclature requirements.
*Forensic Sci. Int. Genet.*2016; 22: 54-63https://doi.org/10.1016/j.fsigen.2016.01.009 - Open source software EuroForMix can be used to analyse complex SNP mixtures.
*Forensic Sci. Int. Genet.*2017; 31: 105-110https://doi.org/10.1016/j.fsigen.2017.08.001 - Massively parallel sequencing analysis of nondegraded and degraded DNA mixtures using the ForenSeq™ system in combination with EuroForMix software.
*Int. J. Leg. Med.*2019; 133: 25-37https://doi.org/10.1007/s00414-018-1961-y - DNA mixture interpretation using linear regression and neural networks on massively parallel sequencing data of single nucleotide polymorphisms.
*Null.*2022; 54: 150-162https://doi.org/10.1080/00450618.2020.1807050

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