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30. juni 2026.

Unutar Genebench-Pro

Detaljniji pogled na referentni test, njegova pitanja i prateće materijale.

Studije slučaja

Ovih 10 studija slučaja prikazuje reprezentativna pitanja iz GeneBench-Pro. Svaka studija slučaja uključuje originalni upit, skupove podataka i prateće materijale. Za pregled referentnog testa i ključnih nalaza pogledajte najavni blog.

Napomena: Pretpregledi datoteka prikazuju isječke iz kompletnih skupova podataka.

Studija slučaja 1

Somatska onkologija: odluka o omjeru koristi i rizika terapije tumora na osnovu strukturnih varijanti

Procijenite da li sintetički inhibitor usmjeren na TXR1 ima pozitivnu kliničku korisnost u tumorima čija je aktivacija cilja vođena strukturnom varijantom. TXR1, TXR1i, DLR1 i oznake star-allel su sintetičke referentne oznake. 

Ciljna podgrupa se mora rekonstruirati na osnovu dokaza iz dugih očitanja, ekspresije, kvaliteta tumora i farmakogenomike prije nego što se korist i toksičnost mogu tumačiti kao odluka o liječenju.

Objavljeni upit prikazan modelu

A molecular tumor board registry contains trial-eligible advanced solid-tumor cases considered for a TXR1-directed inhibitor. Estimate, for tumors with SV-driven TXR1 target-mediated activation at time zero, the marginal effect of TXR1i versus non-TXR1 systemic therapy on week-16 clinical benefit as if all patients had an assessable week-16 visit. Also estimate the 8-week treatment-limiting toxicity/discontinuation risk under TXR1i in the same target population. Report net clinical utility = benefit risk difference (percentage points) - 0.35 * toxicity risk (percentage points), and choose therapy_class_code 1 if TXR1i has positive net utility and 0 otherwise. 

Use percentage-point units for all non-code quantities. Positive benefit means TXR1i improves week-16 clinical benefit relative to non-TXR1 systemic therapy.

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "therapy_class_code": <int>,
4
    "benefit_rd_pp": <float>,
5
    "toxicity_dropout_risk_pp": <float>,
6
    "net_clinical_utility_pp": <float>
7
  },
8
  "reasoning": "<description of method and QC>"
9
}

Datoteke dostavljene modelu

Studija slučaja 2

Funkcionalna genomika: validacija cilja CRISPR-a: transkript lncRNA ili genomski lokus?

Utvrdite da li je prividna zavisnost od lncRNA specifična za transkript ili uvjetovana efektima obližnjeg lokusa i susjednih gena.

Dokazi usmjereni transkriptom moraju izdržati kontrole za lokalnu perturbaciju DNK lokusa, represiju susjednog gena, zamjene vodiča, GC toksičnost i efekte ploče.

Objavljeni upit prikazan modelu

You are given pooled CRISPRi screening data, guide-level local expression measurements, transcript-targeting CasRx follow-up data, and single-guide follow-up growth measurements for a nominated lncRNA program (LINC473) and a nearby coding gene (KIN1). The identifiers LINC473, KIN1, and ANKRD42 are synthetic benchmark labels; any resemblance to real human genes is coincidental.

Estimate the requested quantities.

Definitions:

  • lncrna_specific_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% effective knockdown of the dominant LINC473 transcript, not local DNA-locus effects.

  • neighbor_mediated_lfc: the pooled-screen matched-control day-10 log2 growth effect expected at 100% KIN1 repression in the local LINC473-locus model after accounting for concomitant LINC473 transcript knockdown.

  • advance_target: 1 if the evidence supports advancing LINC473 as a transcript-directed target, else 0.

Conventions:

  • all growth effects are log2(day10/day0) competitive-growth effects relative to matched controls;

  • more negative numbers indicate stronger loss of fitness;

  • set advance_target to 1 only if lncrna_specific_lfc <= -0.08 and neighbor_mediated_lfc > -0.25; otherwise 0.

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "advance_target": <int>,
4
    "lncrna_specific_lfc": <float>,
5
    "neighbor_mediated_lfc": <float>
6
  },
7
  "reasoning": "<description of method and QC>"
8
}

Datoteke dostavljene modelu

Studija slučaja 3

Statistička genetika: Prioritizacija proteinskih meta lijekova u povezanom genetskom lokusu

Procijenite direktne efekte bolesti za dva obližnja proteina koristeći cis multivarijabilnu Mendelovu randomizaciju (cis-MVMR), uz rukovanje skalom analize, orijentacijom alela, prokletstvom pobjednika, LD-om i rezidualnom lokalnom pleiotropijom.

Ta dva proteina dijele korelirani lokus. Analiza mora preći sa marginalnih asocijacija na uslovne efekte bolesti koji uzimaju u obzir LD, na zajedničkoj proteinskoj skali.

Objavljeni upit prikazan modelu

You are given association summary statistics and metadata for two nearby proteins (PROTA and PROTB), a binary disease outcome, a locus correlation reference, and protein measurement records.

Goal: estimate the direct log-odds effect of each protein on the disease outcome per +1 SD increase in log10 concentration, conditional on the other protein.

Interpretation: theta_PROTA and theta_PROTB use the same log-odds per-SD scale defined in the goal.

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "theta_PROTA": <float>,
4
    "theta_PROTB": <float>
5
  },
6
  "reasoning": "<description of method and QC>"
7
}

Datoteke dostavljene modelu

Studija slučaja 4

Klinička genomika / skrining nosilaštva: rezidualni rizik skrininga nosilaštva za DRX1 uz kalibraciju CNV-a i pseudogena

Procijenite učestalosti nosilaštva specifične za porijeklo, rezidualni rizik nakon negativnog rezultata skrininga, učestalost nosilaštva kod partnera i rizik da začeti plod bude zahvaćen na osnovu podataka testa za skrining nosilaštva.

Procjena preostalog rizika zavisi od određivanja statusa nosioca uzimajući u obzir pseudogene, objedinjavanja osnivačkih haplotipova, kalibracije testa specifične za porijeklo i standardizacije od testiranih partnera nazad na kompletan spisak partnera.

Objavljeni upit prikazan modelu

Using cohort_roster.tsv.gz, partner_roster.tsv.gz, calibration_controls.tsv.gz, target_metadata.tsv.gz, and assay_observations.tsv.gz, estimate residual reproductive risk for an autosomal recessive DRX1 condition. Report all quantities on the probability scale, not as percentages: carrier_frequency_afr and carrier_frequency_eur among screening-roster adults; residual_carrier_risk_afr_negative for an AFR screening-roster adult with a negative DRX1 screen; partner_carrier_frequency_full_roster for a uniformly sampled partner_roster.tsv.gz row; and couple_reproductive_risk for an affected conceptus when the index person is AFR and screen-negative and the partner is drawn from partner_roster.tsv.gz. Assume autosomal recessive inheritance with a 1/4 affected-conceptus risk conditional on both biological parents being carriers. 

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "carrier_frequency_afr": <float>,
4
    "carrier_frequency_eur": <float>,
5
    "residual_carrier_risk_afr_negative": <float>,
6
    "partner_carrier_frequency_full_roster": <float>,
7
    "couple_reproductive_risk": <float>
8
  },
9
  "reasoning": "<description of method and QC>"
10
}

Datoteke dostavljene modelu

Studija slučaja 5

Jednoćelijska genomika: eQTL aktiviranih monocita nakon korekcije ambijentalne RNA

Procijenite efekat genotipa na ekspresiju aktiviranih monocita nakon uklanjanja ambijentalne RNA i tehničke kontaminacije iz podataka jednoćelijskog RNA-seq-a.

Ambijentalna RNA utiče i na ekspresiju cilja i na panel markera koji se koristi za određivanje stanja aktivacije, pa se korekcija mora izvršiti prije eQTL modela.

Objavljeni upit prikazan modelu

Estimate the per-allele log rate ratio for CXCL10 expression in the activated monocyte subpopulation from the provided single-cell RNA-seq data. 

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "beta_activated": <float>
4
  },
5
  "reasoning": "<description of method and QC>"
6
}

Datoteke dostavljene modelu

Studija slučaja 6

Strukturna genetika: ugnježđena strukturna varijanta: podrška na osnovu ekspresije i klinička povezanost

Procijenite ima li ugnježđeni strukturni subhaplotip unutar anonimnog lokusa nalik inverziji kalibriranu kliničku povezanost i vjerodostojnu podršku ekspresije.

Ugnježđeni signal doziranja kopija može biti konfundiran širim smjerom inverzije, pa kalibracija doziranja, ekspresijska podrška i kliničko modeliranje moraju ostati odvojeni.

Objavljeni upit prikazan modelu

Analyze the released files for anonymous Locus Q. Estimate the full-cohort source-population clinical association and molecular expression support for the calibrated nested segment-B structural copy dosage, separating the nested segment-B dosage from the broader outer-orientation dosage. Report subhap_log_or as the natural-log source-population total-effect odds ratio for case status per additional calibrated segment-B copy. Report expression_log_fc as the natural-log expression fold-change per calibrated segment-B copy for the expression-supported gene. Report target_support_code as 1 if the supported gene has a positive expression_log_fc and the clinical association is protective (subhap_log_or < 0), otherwise 0. Report n_calibrated_carriers as the number of reliable breakpoint-panel samples carrying at least one segment-B copy. 

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "n_calibrated_carriers": <int>,
4
    "target_support_code": <int>,
5
    "expression_log_fc": <float>,
6
    "subhap_log_or": <float>
7
  },
8
  "reasoning": "<description of method and QC>"
9
}

Datoteke dostavljene modelu

Studija slučaja 7

Regulatorna genomika: Mjerenje jačine hromatinskih petlji nakon maskiranja strukturnih varijanti i artefakata mapiranja

Kvantificiraj fokalnu razliku u jačini Hi-C petlje između slučajeva i kontrola nakon uklanjanja artefakata niske mapabilnosti i strukturnih varijanti iz pozadine očekivanih kontakata.

Ciljna petlja je definirana pri rezoluciji od 20 kb, ali je model očekivanih kontakata iskrivljen osim ako se prvo ne maskiraju kontakti niske mapabilnosti i SV pruga koja se pojavljuje samo u pojedinačnim slučajevima (case-only).

Objavljeni upit prikazan modelu

You are given Hi-C contact matrices at 20 kb and 40 kb resolution plus bin annotations. Estimate the loop enrichment at the 20 kb interaction between `bin_id = 8` and `bin_id = 17` in `bins_20kb.tsv.gz`. Report three quantities: `case_loop_strength` (mean log2(observed/expected) across case replicates), `control_loop_strength` (mean log2(observed/expected) across control replicates), and `delta_loop_strength` (case minus control).

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "case_loop_strength": <float>,
4
    "control_loop_strength": <float>,
5
    "delta_loop_strength": <float>
6
  },
7
  "reasoning": "<description of method and QC>"
8
}

Datoteke dostavljene modelu

Studija slučaja 8

Statistička genetika: višeroditeljsko mapiranje QTL-a uz rekonstrukciju osnivača

Mapirajte lokus kvantitativne osobine na hromozomu 1 u rekombinantnoj populaciji s osam osnivača rekonstruiranjem osnivačkog porijekla prije testiranja povezanosti s fenotipom.

Vidljivi podaci o markerima su bialelni, ali biološki signal je porijeklo osnivača. Valjana analiza stoga mora rekonstruirati stanje osnivača, provjeriti orijentaciju markera i odvojiti QTL od smetajućeg vrha usklađenog sa serijom.

Objavljeni upit prikazan modelu

Map the chromosome 1 QTL in an 8-founder multi-parent population. Report the position (cM) and which founder carries the high-effect allele.

Report high_founder as "F1".."F8".

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
"answer": {
3
"high_founder": "<string>",
4
"qtl_pos_cM": <float>
5
},
6
"reasoning": "<description of method and QC>"
7
}

Data files:

  • markers.tsv.gz: marker metadata

  • founders.tsv.gz: founder alleles at each marker

  • ril_genotypes.npz: observed RIL genotypes (biallelic)

  • phenotypes.tsv.gz: phenotype and covariates

Datoteke dostavljene modelu

Studija slučaja 9

Populacijska genetika: porijeklo specifično za roditelje i vremensko određivanje nedavne genetske primjese

Procijenite udjele porijekla specifične za svakog roditelja i vrijeme nedavne admiksije iz faziranih segmenata lokalnog porijekla, nakon ispravljanja recipročnih artefakata i inverzije oznaka specifične za hromozom.

I frakcije porijekla i vremena pulsnih događaja mijenjaju se ako se artefakti recipročnih traktova, inverzija oznaka na nivou hromozoma ili nazivnici mapa nepravilno obrade.

Objavljeni upit prikazan modelu

You are given phased local-ancestry tracts for one admixed individual. Estimate, for each transmitted parental haplotype, the fraction of ancestry A across the called tract span and the number of generations since a single recent admixture pulse. Label parent1 as the haplotype with the smaller ancestry-A fraction and parent2 as the haplotype with the larger ancestry-A fraction. 

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
  "answer": {
3
    "parent1_A_fraction": <float>,
4
    "parent1_t": <float>,
5
    "parent2_A_fraction": <float>,
6
    "parent2_t": <float>
7
  },
8
  "reasoning": "<description of method and QC>"
9
}

Datoteke dostavljene modelu

Studija slučaja 10

Populacijska genetika: procjena selekcije iz zašumljenih vremenskih serija drevne DNK

Odredi koji je od dva haploidna lokusa pod snažnijom pozitivnom selekcijom na osnovu drevnih vremenskih serija frekvencija alela, uzimajući u obzir orijentaciju alela, grešku smjera, genetički drift i promjenjivu veličinu populacije.

Šumne drevne putanje nisu direktno uporedive dok se oba lokusa ne postave na istu skalu izvedenog alela i dok se navedene vrijednosti greške sekvenciranja na nivou uzorka ne modeliraju direktno.

You are given allele-frequency time series data from two haploid loci sampled over multiple generations.

One locus is under stronger positive selection than the other. Estimate the selection coefficient s for the more strongly selected locus, where s > 0 means the derived allele is favored.

Assume instrument-driven sequencing error is ~1%. The seq_error column is the average of the two directional allele-miscall rates for that locus and sample.

The selected_locus value must be "A" or "B".

These data came from a real experiment; you will be graded not just on numerical correctness but the quality of analytical reasoning you exhibit; do not attempt to take any shortcuts.

Return your final answer as exactly one JSON object.
Do not wrap the JSON in markdown.
Do not add prose before or after the JSON.
Do not omit any keys shown in the example.
Return the JSON object in your final answer:

JSON

1
{
2
"answer": {
3
"selected_locus": "<string>",
4
"s": <float>
5
},
6
"reasoning": "<description of method and QC>"
7
}

Datoteke dostavljene modelu