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OpenAI

30 Juni 2026

Ndani ya Genebench-Pro

Uchunguzi wa kina zaidi wa kipimo cha kulinganisha, maswali yake, na nyenzo saidizi.

Uchunguzi wa kesi

Chunguzi hizi za kesi 10 zinaonyesha maswali wakilishi kutoka GeneBench-Pro. Kila uchunguzi kifani unajumuisha dokezo asilia, seti za data, na nyenzo za kusaidia. Kwa muhtasari wa kipimo linganishi na matokeo muhimu, tazama blogu ya matangazo.

Kumbuka: Maonyesho ya awali ya faili yanaonyesha dondoo kutoka kwenye seti kamili za data.


Uchunguzi kifani wa 1

Onkolojia ya somatiki: Uamuzi wa uwiano wa manufaa na hatari wa tiba ya uvimbe unaoongozwa na tofauti za kimuundo

Kadiria kama kizuizi sintetiki kinacholenga TXR1 kina manufaa chanya ya kitabibu katika uvimbe ambao uanzishaji wa shabaha yake unasababishwa na tofauti ya kimuundo. TXR1, TXR1i, DLR1, na lebo za star-allele ni lebo bandia za kipimo linganishi. 

Ni lazima kikundi kidogo kinacholengwa kibainishwe kutokana na ushahidi wa usomaji mrefu, uonyeshaji wa jeni, ubora wa uvimbe, na famakojenomiki kabla ya manufaa na sumu kufasiriwa kama uamuzi wa matibabu.

Dokezo lililotolewa linaloonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

patient_idanalysis_setagesexsitecalendar_periodecogtumor_burdenprior_linesprior_resistancelineage_classtherapy_classassessed16benefit16tox_stop_8wktime_zero_day
MTB0001173.8MS1P220.78731ATXR1i010
MTB0002155.2MS3P112.63701ATXR1i1000
MTB0003168.8FS4P200.89121ATXR1i1110
MTB0004182.8FS2P224.10100BTXR1i1000
MTB0005165.5FS1P317.011ATXR1i1000

Vigezo vya sajili, tiba, tathmini ya wiki ya 16, faida, na sumu ya mapema.


Uchunguzi kifani wa 2

Jenomiki ya Kiutendaji: Uthibitisho Lengwa wa CRISPR: Nakala ya lncRNA au Eneo la Jenomu?

Amua kama utegemezi unaoonekana wa lncRNA ni mahususi kwa nakala au unasababishwa na athari za jeni za eneo jirani la karibu.

Ushahidi unaoelekezwa na nakala lazima ubaki thabiti baada ya kutumia vidhibiti vya usumbufu wa eneo la DNA, ukandamizaji wa jeni jirani, mabadiliko ya mwongozo, sumu ya GC, na athari za visahani vya majaribio.

Dokezo lililotolewa lililoonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

guide_idnominal_targetchrcoordstranddist_lnc_tss_bpdist_neighbor_tss_bpguide_gc_frac
g001LINC473chr7100014+14300.624
g002LINC473chr7100035-43670.584
g003LINC473chr7100051+116560.622
g004LINC473chr7100066-59660.617
g005LINC473chr7100088+74770.715

Viwianishi, malengo, umbali, na vipengele vya GC vinavyoelekezwa.


Uchunguzi kifani wa 3

Jenetiki za kitakwimu: Kuipa kipaumbele malengo ya dawa za protini katika eneo la kijeni lililounganishwa

Kadiria athari za moja kwa moja za ugonjwa kwa protini mbili zilizo karibu kwa kutumia ubahatishaji wa Mendelian wa cis wenye vigeu vingi (cis-MVMR), huku ukishughulikia kipimo cha uchanganuzi, mwelekeo wa aleli, laana ya mshindi, LD, na pleiotropia ya ndani iliyobaki.

Protini hizi mbili zinashiriki eneo lenye uhusiano. Uchanganuzi lazima uhame kutoka kwenye uhusiano wa pembezoni hadi kwenye athari za ugonjwa za masharti, zinazotilia maanani LD, kwa kipimo cha pamoja cha protini.

Dokezo lililotolewa linaloonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

snppos_bpeffect_alleleother_allelemafbetasepval
rs20000050000000AC0.422150.0064386683107068080.0032673300912034120.04876727714241972
rs20000150010126AC0.057090.0110089933375813010.0069552392087504070.11345916603941006
rs20000250020253GT0.090210.0099220147571163190.0056330230270155180.07817048492026045
rs20000350030379GT0.483990.0105692156141645730.00322914197402374450.0010638520681901973
rs20000450040506AG0.377030.0070365513782386540.00332975923212698020.034580976884336506

Mihtasari ya uhusiano wa protini katika hatua ya uchunguzi wa PROTA.


Uchunguzi kifani wa 4

Uchunguzi wa jenomu/uchunguzi wa kimatibabu: Hatari iliyobaki ya uchunguzi wa uchukuzi wa DRX1 chini ya CNV na urekebishaji wa jeni bandia

Kadiria mara kwa mara za mbebaji wa asili, hatari iliyobaki baada ya skrini hasi, mara za mbebaji mwenza, na hatari ya mtoto aliyeathirika kutokana na data ya jaribio la uchunguzi wa mbebaji.

Kadirio la hatari iliyosalia linategemea ubainishaji wa ubebaji unaozingatia jeni bandia, uunganishaji wa haplotipu za waanzilishi, urekebishaji wa kipimo kulingana na asili ya kinasaba, na usanifishaji kutoka kwa wenzi waliopimwa hadi kwenye orodha kamili ya wenzi.

Dokezo lililotolewa lililoonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

sample_idcollectionancestryfamily_history_tier
S_EUR_0001screeningEUR0
S_EUR_0002screeningEUR0
S_EUR_0003screeningEUR0
S_EUR_0004screeningEUR0
S_EUR_0005screeningEUR1

Watu wazima kwenye orodha ya uchunguzi wenye asili ya ukoo na muktadha wa uchunguzi.


Uchunguzi kifani wa 5

Jenomiki ya seli moja: eQTL ya monosaiti zilizowashwa baada ya usahihishaji wa RNA ya mazingira

Kadiria athari ya jenotipu kwenye ujielezaji wa monosaiti zilizoamilishwa baada ya kuondoa RNA ya mazingira na uchafuzi wa kiufundi kutoka kwenye data ya RNA-seq ya seli moja.

RNA ya mazingira huathiri ujielezaji lengwa na paneli ya viashiria inayotumiwa kubainisha hali ya uamilishaji, kwa hivyo usahihishaji lazima ufanyike kabla ya muundo wa eQTL.

Dokezo lililotolewa lililoonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

cell_iddonortotal_umiHBBIFI6ISG15LST1CXCL10
D01_C001D011113734835
D01_C002D01110363311210
D01_C003D0111419812639
D01_C004D01125076043217
D01_C005D0110459125115

Idadi za UMI kwa kila seli kwa ajili ya jeni za viashiria, viashiria vya uchafuzi, na jeni lengwa.


Uchunguzi kifani wa 6

Jenetiki ya Kimuundo: Tofauti ya Kimuundo Iliyopachikwa: Ushahidi wa Uonyeshaji na Uhusiano wa Kitabibu

Kadiria kama aina ndogo ya kimuundo iliyojengeka ndani ya eneo lisilojulikana linalofanana na ubadilishaji lina uhusiano wa kimatibabu uliosawazishwa na usaidizi wa usemi unaoaminika.

Ishara ya kipimo cha nakala iliyounganishwa inaweza kuchanganyikiwa na mwelekeo mpana wa ubadilishaji, kwa hivyo urekebishaji wa kipimo, usaidizi wa usemi, na uundaji wa modeli za kimatibabu lazima zibaki tofauti.

Dokezo lililotolewa linaloonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

sample_idcaseageage_bandsexpc1pc2pc3ancestry_groupclinic_stratumrecruitment_stream
Q00012150.4550_640-1.01514-0.21032-0.08849EURtertiaryclinic
Q00028057.3950_640-1.25987-0.124980.2344EURregionalregistry
Q00029168.465_plus00.915980.621770.01891AFRtertiaryclinic
Q00030174.0765_plus10.21125-0.59634-0.08197EAScommunityregistry
Q00032182.8265_plus0-1.12034-0.243720.14665EURcommunityclinic

Takwimu za kitabibu na vigeu kwa kundi zima.


Uchunguzi kifani wa 7

Jenomiki ya udhibiti: Kupima nguvu ya kitanzi cha kromatini baada ya kufunika vibadala vya kimuundo na vizalia vya upangaji ramani

Kadiria tofauti ya nguvu ya kitanzi cha Hi-C cha eneo mahususi kati ya visa na udhibiti, baada ya kuondoa vizalia vya uwezo mdogo wa ulinganishaji kwenye ramani na vya vibadala vya kimuundo kutoka kwenye usuli wa migusano inayotarajiwa.

Kitanzi lengwa kinafafanuliwa kwa ubora wa 20 kb, lakini muundo wa migusano inayotarajiwa hupotoshwa isipokuwa migusano yenye uwezo mdogo wa kuwekewa ramani na mstari wa SV wa kesi pekee zifichwe kwanza.

Dokezo lililotolewa linaloonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwenye muundo

bin_idchromstartendgc_contentmappabilityre_sites
0chr84000004200000.461990338215725940.97875742147042735
1chr84200004400000.50441242085346770.89010849434983975
2chr84400004600000.432184515849381940.90568792893267123
3chr84600004800000.47331972826812180.93765298406647893
4chr84800005000000.44449560621507480.86825655179818774

Maelezo ya vikundi vya azimio lengwa.


Uchunguzi kifani wa 8

Jenetiki ya kitakwimu: Uchoraji Ramani wa QTL wa Wazazi Wengi kwa Ujenzi Upya wa waanzilishi

Ramani ya eneo la sifa ya kiasi ya kromosomu-1 katika kundi la waanzilishi wanane walioungana tena kwa kujenga upya asili ya waanzilishi kabla ya kujaribu uhusiano wa fenotipu.

Data za viashirio vinavyoonekana zina aleli mbili, lakini ishara ya kibiolojia ni nasaba ya waanzilishi. Kwa hiyo, uchanganuzi unaoweza kutetewa lazima ujenge upya hali ya waanzilishi, ukague mwelekeo wa viashirio, na utenganishe QTL na kilele cha usumbufu kinachoendana na kundi.

Dokeza lililotolewa linaloonyeshwa kwenye muundo

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

Faili zilizotolewa kwenye muundo

marker_idchrpos_cM
m2_065259.762431265596575
m2_103294.52656615104739
m2_107298.18761427503033
m2_079272.20130244108847
m1_054149.907510212292195

Vitambulishi vya alama, kromosomu, na nafasi za ramani ya kijeni.


Uchunguzi kifani wa 9

Jenetiki ya idadi ya watu: Asili ya kinasaba mahususi kwa mzazi na wakati wa mchanganyiko wa kinasaba wa hivi karibuni

Kadiria uwiano wa asili ya kinasaba mahususi kwa kila mzazi na muda wa mchanganyiko wa hivi karibuni wa vinasaba kutoka kwenye vipande vya asili ya kinasaba ya eneo la jenomu vilivyopangwa kwa awamu, baada ya kurekebisha vizalia vya pande mbili na ugeuzaji wa lebo mahususi kwa kromosomu.

Visehemu vya uasilia na nyakati za mipigo vyote hubadilika ikiwa vizalia vya vipande vya kurejea, ugeuzaji wa lebo katika eneo mahususi la kromosomu, au denominata za ramani zitashughulikiwa kimakosa.

Dokezo lililotolewa lililoonyeshwa kwenye muundo

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
}

Faili zilizotolewa kwa muundo

chromhapstart_morganend_morganancposteriorlow_complexity_frac
chr1h10.030.505A0.9850.08
chr1h10.5050.535B0.620.92
chr1h10.5351.478849A0.9850.08
chr1h11.5037271.852681B0.9850.08
chr1h11.8526812.422373A0.9850.08

Vipande vya asili ya kinasaba ya kieneo vilivyopangwa kwa awamu, vyenye viwianishi, lebo za asili, thamani za baada ya tathmini, na maelezo ya udhibiti wa ubora.


Uchunguzi kifani wa 10

Jenetiki ya idadi ya viumbe: Kukadiria uteuzi kutoka mfululizo wa muda wa DNA ya kale wenye usumbufu

Bainisha ni ipi kati ya maeneo mawili ya haploidi ambayo yako chini ya uteuzi chanya wenye nguvu zaidi kutoka kwa mfululizo wa nyakati wa kale wa marudio ya aleli, huku ukizingatia mwelekeo wa aleli, kosa la mwelekeo, mkengeuko wa kijeni, na ukubwa wa idadi ya watu unaobadilika.

Njia za kale zenye kelele hazilinganishwi moja kwa moja hadi maeneo yote mawili ya jenomu ya aleli yaliyotokana yawekwe kwenye kiwango kimoja, na thamani za makosa ya upangaji mfuatano katika sampuli zifanywe mfano moja kwa moja.

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
}

Faili zilizotolewa kwenye muundo

generationalt_readstotal_readsseq_errorsample_year
636400.16-4500
1234450.16-4278
1841550.16-4056
2438700.16-3833
3036900.16-3611

Msururu wa wakati wa idadi ya usomaji kwa eneo A.