---
id: "claim:long-read-sequencing-transcriptome-quantification-with-lr-kallisto-2025-12-01-p-c105976de1:76af058cb5e67506c2e4"
kind: "claim"
title: "lr-kallisto used 3–10 times less memory than Oarfish."
description: ""
url: "https://sina.bio/claim/lr-kallisto-used-310-times-less-memory-than-oarfish"
---

# lr-kallisto used 3–10 times less memory than Oarfish.

## Content

lr-kallisto used 3–10 times less memory than Oarfish.

## Metadata

```json
{
  "claim_position": 61,
  "source_publication_id": "version:long-read-sequencing-transcriptome-quantification-with-lr-kallisto--2025-12-01-plos-computational-biology",
  "source_work_id": "work:long-read-sequencing-transcriptome-quantification-with-lr-kallisto"
}
```

## Connections (2)

### Long-read sequencing transcriptome quantification with lr-kallisto

- ID: `version:long-read-sequencing-transcriptome-quantification-with-lr-kallisto--2025-12-01-plos-computational-biology`
- Kind: `publication`
- Direction: `incoming`
- URL: <https://sina.bio/publication/long-read-sequencing-transcriptome-quantification-with-lr-kallisto-3ccc217c>

This publication contains this grounded claim.

> We further bench- marked walltime and maximum resident set size of bambu, IsoQuant, lr-kallisto, and oarfish on the exome capture data, showing that lr-kallisto is 3-10x more memory efficient than oarfish, 18-50x more memory efficent than bambu, and 15-46x more memory efficient than IsoQuant (Table A in S1 Text).

Source: 2025-12-01-plos-computational-biology.pdf — <https://sina.bio/source/2025-12-01-plos-computational-biology-pdf>

### lr-kallisto is faster and uses substantially less memory than compared long-read quantification tools while retaining ka

- ID: `result:long-read-sequencing-transcriptome-quantification-with-lr-kallisto-2025-12-01-p-c105976de1:0ed2277f83f4c7c9a656`
- Kind: `result`
- Direction: `incoming`
- URL: <https://sina.bio/result/lr-kallisto-is-faster-and-uses-substantially-less-memory-than-compared-01127e3d>

This claim supports this result.